For the latest information on the checks performed in pedantic mode -see the Pedantic +see the Pedantic mode chapter in the Stan Reference Manual.
Pedantic mode is available when compiling the model or when using the
separate $check_syntax() method of a
@@ -278,7 +278,7 @@
Pedantic check$compile() method directly if using
the delayed compilation approach described above).
mod_pedantic <- cmdstan_model(stan_file_pedantic, pedantic = TRUE)
-Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d863015ed4.stan', line 11, column 14: A
+Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-74281eb75594.stan', line 11, column 14: A
poisson distribution is given parameter lambda as a rate parameter
(argument 1), but lambda was not constrained to be strictly positive.
Warning: The parameter lambda has no priors. This means either no prior is
@@ -288,7 +288,7 @@ Pedantic checkpedantic
argument to the $check_syntax()
method.
mod_pedantic$check_syntax(pedantic = TRUE)
-Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
+Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
poisson distribution is given parameter lambda as a rate parameter
(argument 1), but lambda was not constrained to be strictly positive.
Warning: The parameter lambda has no priors. This means either no prior is
@@ -305,7 +305,7 @@ Pedantic check
mod_pedantic <- cmdstan_model(stan_file_pedantic, compile = FALSE)
mod_pedantic$check_syntax(pedantic = TRUE)
-Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
+Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
poisson distribution is given parameter lambda as a rate parameter
(argument 1), but lambda was not constrained to be strictly positive.
Warning: The parameter lambda has no priors. This means either no prior is
@@ -495,10 +495,10 @@ Default temporary files
fit$output_files()
-[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-1-36f995.csv"
-[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-2-36f995.csv"
-[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-3-36f995.csv"
-[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-4-36f995.csv"
+[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-1-37e810.csv"
+[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-2-37e810.csv"
+[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-3-37e810.csv"
+[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-4-37e810.csv"
These files will be lost if you end your R session or if you remove
the fit object and force (or wait for) garbage
collection.
@@ -510,8 +510,8 @@ Default temporary filesrm(fit)
gc()
used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
-Ncells 1249060 66.8 2424964 129.6 NA 2424964 129.6
-Vcells 2192048 16.8 8388608 64.0 32768 3407356 26.0
+Ncells 1260600 67.4 2436292 130.2 NA 2436292 130.2
+Vcells 2212085 16.9 8388608 64.0 32768 3459642 26.4
file.exists(files)
[1] FALSE FALSE FALSE FALSE
@@ -561,14 +561,14 @@ Lazy CSV readingLazy CSV reading
After we call a method that requires the draws then if we reexamine
@@ -623,14 +623,14 @@
Lazy CSV readingLazy CSV readingLazy CSV reading
For models with many parameters, transformed parameters, or generated
@@ -688,7 +688,7 @@
read_cmdstan_csv()read_cmdstan_csv()read_cmdstan_csv()read_cmdstan_csv()Saving and
fit <- mod$sample(data = data_list, save_latent_dynamics = TRUE)
fit$latent_dynamics_files()
-[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-1-460fca.csv"
-[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-2-460fca.csv"
-[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-3-460fca.csv"
-[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-4-460fca.csv"
+[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-diagnostic-202312131009-1-425883.csv"
+[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-diagnostic-202312131009-2-425883.csv"
+[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-diagnostic-202312131009-3-425883.csv"
+[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-diagnostic-202312131009-4-425883.csv"
# read one of the files in
x <- utils::read.csv(fit$latent_dynamics_files()[1], comment.char = "#")
head(x)
lp__ accept_stat__ stepsize__ treedepth__ n_leapfrog__ divergent__
-1 -6.84314 0.979774 0.874237 1 3 0
-2 -7.10483 0.971803 0.874237 2 3 0
-3 -6.85492 1.000000 0.874237 1 1 0
-4 -6.81455 0.951680 0.874237 2 3 0
-5 -6.90994 0.917527 0.874237 2 3 0
-6 -6.75277 1.000000 0.874237 1 3 0
- energy__ theta p_theta g_theta
-1 6.84887 -0.814342 0.139388 0.683591
-2 7.10784 -0.558031 -0.101020 1.368040
-3 7.03534 -0.797632 0.782091 0.726389
-4 7.12956 -0.860038 1.033420 0.567976
-5 7.27609 -0.730035 -1.114150 0.902245
-6 6.87890 -1.033970 -0.653912 0.147785
+1 -9.73111 1.000000 0.932232 2 3 0
+2 -6.75346 1.000000 0.932232 1 3 0
+3 -8.48107 0.665681 0.932232 1 3 0
+4 -7.74234 1.000000 0.932232 1 1 0
+5 -6.82333 1.000000 0.932232 2 3 0
+6 -6.82333 0.784555 0.932232 1 3 0
+ energy__ theta p_theta g_theta
+1 10.97150 0.3939970 -2.155610 4.166930
+2 9.05119 -1.0295100 -2.933880 0.158161
+3 8.61284 0.0530292 0.702591 3.159050
+4 8.45793 -0.2148440 1.637290 2.357940
+5 7.76204 -1.3633100 1.875250 -0.555575
+6 8.04989 -1.3633100 -2.143570 -0.555575
The column lp__ is also provided via
fit$draws(), and the columns accept_stat__,
stepsize__, treedepth__,
@@ -818,13 +818,13 @@
Saving and
unique to the latent dynamics file.
- theta p_theta g_theta
-1 -0.814342 0.139388 0.683591
-2 -0.558031 -0.101020 1.368040
-3 -0.797632 0.782091 0.726389
-4 -0.860038 1.033420 0.567976
-5 -0.730035 -1.114150 0.902245
-6 -1.033970 -0.653912 0.147785
+ theta p_theta g_theta
+1 0.3939970 -2.155610 4.166930
+2 -1.0295100 -2.933880 0.158161
+3 0.0530292 0.702591 3.159050
+4 -0.2148440 1.637290 2.357940
+5 -1.3633100 1.875250 -0.555575
+6 -1.3633100 -2.143570 -0.555575
Our model has a single parameter theta and the three
columns above correspond to theta in the
unconstrained space (theta on the constrained
@@ -872,16 +872,16 @@
Troubleshooting and debuggingmod <- cmdstan_model(stan_file, force_recompile = TRUE)
Running make \
- /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc \
+ /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467 \
"STANCFLAGS += --name='bernoulli_model'"
--- Translating Stan model to C++ code ---
-bin/stanc --name='bernoulli_model' --o=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.hpp /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.stan
+bin/stanc --name='bernoulli_model' --o=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.hpp /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.stan
--- Compiling, linking C++ code ---
-clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -c -include-pch stan/src/stan/model/model_header_13_0.hpp.gch -x c++ -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.hpp
-clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.o src/cmdstan/main.o -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc
-rm -f /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.o
+clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -c -include-pch stan/src/stan/model/model_header_13_0.hpp.gch -x c++ -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.hpp
+clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.o src/cmdstan/main.o -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467
+rm -f /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.o
fit <- mod$sample(
data = data_list,
@@ -891,11 +891,11 @@ Troubleshooting and debugging)
Running MCMC with 1 chain...
-Running ./bernoulli 'id=1' random 'seed=2046085541' data \
- 'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/standata-131d874013405.json' \
+Running ./bernoulli 'id=1' random 'seed=90958316' data \
+ 'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/standata-742874baeb02.json' \
output \
- 'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081036-1-2ef601.csv' \
- 'profile_file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-profile-202311081036-1-0c4891.csv' \
+ 'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-1-4db120.csv' \
+ 'profile_file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-profile-202312131009-1-1c1f83.csv' \
'method=sample' 'num_samples=100' 'num_warmup=100' 'save_warmup=0' \
'algorithm=hmc' 'engine=nuts' adapt 'engaged=1'
Chain 1 method = sample (Default)
@@ -925,19 +925,19 @@ Troubleshooting and debuggingSampler diagnostic warnings a
suffers from divergences.
fit_with_warning <- cmdstanr_example("schools")
-Warning: 124 of 4000 (3.0%) transitions ended with a divergence.
-See https://mc-stan.org/misc/warnings for details.
-Warning: 1 of 4 chains had an E-BFMI less than 0.2.
+Chain 4 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+Chain 4 Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmpl3ZPjU/model-76ac71f3c1fe.stan', line 14, column 2 to column 41)
+Chain 4 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+Chain 4 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+Chain 4
+Warning: 149 of 4000 (4.0%) transitions ended with a divergence.
See https://mc-stan.org/misc/warnings for details.
After fitting there is a warning about divergences. We can also
regenerate this warning message later using
fit$diagnostic_summary().
-
+
diagnostics <- fit_with_warning$diagnostic_summary()
-Warning: 124 of 4000 (3.0%) transitions ended with a divergence.
-See https://mc-stan.org/misc/warnings for details.
-Warning: 1 of 4 chains had an E-BFMI less than 0.2.
+Warning: 149 of 4000 (4.0%) transitions ended with a divergence.
See https://mc-stan.org/misc/warnings for details.
-
+
print(diagnostics)
$num_divergent
-[1] 3 12 97 12
+[1] 11 54 32 52
$num_max_treedepth
[1] 0 0 0 0
$ebfmi
-[1] 0.20 0.29 0.29 0.43
-
+[1] 0.40 0.36 0.37 0.30
+
# number of divergences reported in warning is the sum of the per chain values
sum(diagnostics$num_divergent)
-[1] 124
+[1] 149
CmdStan’s diagnose utility
@@ -543,7 +544,7 @@ Create a stanfit object
+
stanfit <- rstan::read_stan_csv(fit$output_files())
@@ -561,26 +562,26 @@ Optimization$optimize().
-
+
fit_mle <- mod$optimize(data = data_list, seed = 123)
Initial log joint probability = -9.51104
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
6 -5.00402 0.000103557 2.55661e-07 1 1 9
Optimization terminated normally:
Convergence detected: relative gradient magnitude is below tolerance
-Finished in 0.1 seconds.
-
+Finished in 0.2 seconds.
+
fit_mle$print() # includes lp__ (log prob calculated by Stan program)
variable estimate
lp__ -5.00
theta 0.20
-
+
fit_mle$mle("theta")
theta
0.2
Here’s a plot comparing the penalized MLE to the posterior
distribution of theta.
-
+
@@ -591,7 +592,7 @@ OptimizationMaximum
Likelihood Estimation section of the CmdStan User’s Guide for more
details.
-
+
fit_map <- mod$optimize(
data = data_list,
jacobian = TRUE,
@@ -620,7 +621,7 @@ Laplace Approximationmode
argument. If mode is omitted then
optimization will be run internally before taking draws from the normal
approximation.
-
+
fit_laplace <- mod$laplace(
mode = fit_map,
draws = 4000,
@@ -636,11 +637,11 @@ Laplace Approximation
-
+
fit_laplace$print("theta")
variable mean median sd mad q5 q95
theta 0.27 0.25 0.12 0.12 0.10 0.50
-
+
mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025)
@@ -651,7 +652,7 @@ Variational (ADVI)$variational()
method. For details on the ADVI algorithm see the CmdStan
User’s Guide.
-
+
fit_vb <- mod$variational(
data = data_list,
seed = 123,
@@ -662,8 +663,8 @@ Variational (ADVI)Variational (ADVI)
+
fit_vb$print("theta")
variable mean median sd mad q5 q95
theta 0.27 0.25 0.12 0.12 0.10 0.49
-
+
mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025)
@@ -696,7 +697,7 @@ Variational (Pathfinder)CmdStan
User’s Guide. Pathfinder is run using the $pathfinder()
method.
-
+
fit_pf <- mod$pathfinder(
data = data_list,
seed = 123,
@@ -720,7 +721,7 @@ Variational (Pathfinder)
-
+
fit_pf$print("theta")
variable mean median sd mad q5 q95
theta 0.25 0.24 0.12 0.12 0.08 0.47
@@ -728,22 +729,22 @@ Variational (Pathfinder)
-
+
mcmc_hist(fit_pf$draws("theta"), binwidth = 0.025) +
ggplot2::labs(subtitle = "Approximate posterior from pathfinder") +
ggplot2::xlim(0, 1)
-
+
mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025) +
ggplot2::labs(subtitle = "Approximate posterior from variational") +
ggplot2::xlim(0, 1)
-
+
mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025) +
ggplot2::labs(subtitle = "Approximate posterior from Laplace") +
ggplot2::xlim(0, 1)
-
+
mcmc_hist(fit$draws("theta"), binwidth = 0.025) +
ggplot2::labs(subtitle = "Posterior from MCMC") +
ggplot2::xlim(0, 1)
@@ -767,7 +768,7 @@ Saving fitted model objects
+
fit$save_object(file = "fit.RDS")
# can be read back in using readRDS
@@ -779,7 +780,7 @@ Saving fitted model objects$save_object()
and replace saveRDS with the much faster
qsave() function from the qs package.
-
+
# Load CmdStan output files into the fitted model object.
fit$draws() # Load posterior draws into the object.
try(fit$sampler_diagnostics(), silent = TRUE) # Load sampler diagnostics.
@@ -795,7 +796,7 @@ Saving fitted model objects
+
# Load posterior draws into the fitted model object and omit other output.
fit$draws()
@@ -828,8 +829,8 @@ Advantages of RStanDeveloping
+CRAN. (Note: As of 2023, this can mostly be achieved with CmdStanR as
+well. See Developing
using CmdStanR.)
Avoids use of R6 classes, which may result in more familiar
syntax for many R users.
Summary statistics
fit <- cmdstanr::cmdstanr_example("schools", method = "sample")
-Warning: 191 of 4000 (5.0%) transitions ended with a divergence.
+Warning: 411 of 4000 (10.0%) transitions ended with a divergence.
See https://mc-stan.org/misc/warnings for details.
-
+Warning: 1 of 4 chains had an E-BFMI less than 0.2.
+See https://mc-stan.org/misc/warnings for details.
+
fit$summary()
- variable mean median sd mad q5 q95
-1 lp__ -57.917364 -58.126700 5.227350 5.348035 -66.0514450 -47.99920
-2 mu 6.421661 6.560445 3.994261 3.728717 -0.1207198 13.16044
-3 tau 5.211890 4.447485 3.457118 3.180740 1.0590100 12.09968
-4 theta[1] 9.159825 8.066620 6.731183 5.516199 0.1858543 21.43202
-5 theta[2] 6.750832 6.740980 5.320603 4.835678 -1.7502315 15.66325
-6 theta[3] 5.564623 5.963075 6.485297 5.310451 -5.2589090 15.65180
-7 theta[4] 6.506969 6.552735 5.753473 4.933870 -2.8831600 15.96813
-8 theta[5] 4.602639 4.969540 5.596007 4.792030 -5.1313190 13.08000
-9 theta[6] 5.451744 5.604770 5.781909 4.976021 -4.4380795 14.88214
-10 theta[7] 9.105948 8.421645 6.077003 5.363817 0.4464001 20.13298
-11 theta[8] 6.904986 6.666570 6.591653 5.244594 -3.4154830 18.00026
- rhat ess_bulk ess_tail
-1 1.037320 87.01265 18.13542
-2 1.019548 741.86144 1129.22230
-3 1.041517 74.40670 16.64168
-4 1.012043 615.84853 1757.06290
-5 1.008696 1279.50483 2218.60618
-6 1.002604 1345.71326 1786.80529
-7 1.010044 1291.76655 1900.28317
-8 1.008882 1209.92612 1848.18547
-9 1.034876 1464.53969 1882.25434
-10 1.008780 554.66658 1685.21958
-11 1.034006 1102.79917 1784.94313
+ variable mean median sd mad q5 q95
+1 lp__ -55.581147 -56.536200 7.135791 7.809299 -66.108490 -44.17410
+2 mu 6.053657 5.516030 3.777403 3.597633 -0.119312 12.18247
+3 tau 4.456447 3.456865 3.641807 3.645828 0.778481 11.91315
+4 theta[1] 8.436941 7.287125 6.350694 4.628344 0.249281 20.38983
+5 theta[2] 6.522999 5.993620 5.308671 4.494072 -1.760367 15.64359
+6 theta[3] 5.147676 4.349235 6.014958 5.082679 -5.803791 14.17618
+7 theta[4] 6.189408 5.541310 5.578086 4.501730 -2.557880 15.63053
+8 theta[5] 4.669689 4.952550 5.338198 4.271882 -5.029484 12.96141
+9 theta[6] 5.123046 4.853110 5.623562 4.579796 -4.797084 13.70336
+10 theta[7] 8.283195 7.324155 5.703079 4.912514 1.112880 18.94623
+11 theta[8] 6.499894 5.594240 6.114406 4.839688 -2.747390 16.75831
+ rhat ess_bulk ess_tail
+1 1.348190 9.585696 NA
+2 1.094243 190.870204 910.104440
+3 1.305410 10.562073 4.833756
+4 1.037209 205.819798 1097.512010
+5 1.234963 615.265251 1158.714996
+6 1.270209 429.358164 1118.125260
+7 1.060915 288.566093 1331.398737
+8 1.265317 356.169676 1352.544578
+9 1.281493 482.412281 1311.140711
+10 1.041368 75.113061 938.699910
+11 1.069623 436.937773 1068.232198
By default all variables are summaries with the follow functions:
-
+
posterior::default_summary_measures()
[1] "mean" "median" "sd" "mad" "quantile2"
To change the variables summarized, we use the variables argument
-
+
fit$summary(variables = c("mu", "tau"))
- variable mean median sd mad q5 q95 rhat
-1 mu 6.421661 6.560445 3.994261 3.728717 -0.1207198 13.16044 1.019548
-2 tau 5.211890 4.447485 3.457118 3.180740 1.0590100 12.09968 1.041517
- ess_bulk ess_tail
-1 741.8614 1129.22230
-2 74.4067 16.64168
+ variable mean median sd mad q5 q95 rhat
+1 mu 6.053657 5.516030 3.777403 3.597633 -0.119312 12.18247 1.094243
+2 tau 4.456447 3.456865 3.641807 3.645828 0.778481 11.91315 1.305410
+ ess_bulk ess_tail
+1 190.87020 910.104440
+2 10.56207 4.833756
We can additionally change which functions are used
-
+
fit$summary(variables = c("mu", "tau"), mean, sd)
variable mean sd
-1 mu 6.421661 3.994261
-2 tau 5.211890 3.457118
+1 mu 6.053657 3.777403
+2 tau 4.456447 3.641807
To summarize all variables with non-default functions, it is
necessary to set explicitly set the variables argument, either to
NULL or the full vector of variable names.
-
+
fit$metadata()$model_params
[1] "lp__" "mu" "tau" "theta[1]" "theta[2]" "theta[3]"
[7] "theta[4]" "theta[5]" "theta[6]" "theta[7]" "theta[8]"
-
+
fit$summary(variables = NULL, "mean", "median")
variable mean median
-1 lp__ -57.917364 -58.126700
-2 mu 6.421661 6.560445
-3 tau 5.211890 4.447485
-4 theta[1] 9.159825 8.066620
-5 theta[2] 6.750832 6.740980
-6 theta[3] 5.564623 5.963075
-7 theta[4] 6.506969 6.552735
-8 theta[5] 4.602639 4.969540
-9 theta[6] 5.451744 5.604770
-10 theta[7] 9.105948 8.421645
-11 theta[8] 6.904986 6.666570
+1 lp__ -55.581147 -56.536200
+2 mu 6.053657 5.516030
+3 tau 4.456447 3.456865
+4 theta[1] 8.436941 7.287125
+5 theta[2] 6.522999 5.993620
+6 theta[3] 5.147676 4.349235
+7 theta[4] 6.189408 5.541310
+8 theta[5] 4.669689 4.952550
+9 theta[6] 5.123046 4.853110
+10 theta[7] 8.283195 7.324155
+11 theta[8] 6.499894 5.594240
Summary functions can be specified by character string, function, or
using a formula (or anything else supported by
rlang::as_function()). If these arguments are named, those
names will be used in the tibble output. If the summary results are
named they will take precedence.
-
+
my_sd <- function(x) c(My_SD = sd(x))
fit$summary(
c("mu", "tau"),
@@ -230,45 +232,45 @@ Summary statistics~quantile(.x, probs = c(0.1, 0.9)),
Minimum = function(x) min(x)
)
- variable MEAN median My_SD 10% 90% Minimum
-1 mu 6.421661 6.560445 3.994261 1.488318 11.54665 -15.442700
-2 tau 5.211890 4.447485 3.457118 1.488050 10.02499 0.822982
+ variable MEAN median My_SD 10% 90% Minimum
+1 mu 6.053657 5.516030 3.777403 1.565533 10.863700 -8.554010
+2 tau 4.456447 3.456865 3.641807 0.778481 9.666494 0.774844
Arguments to all summary functions can also be specified with
.args.
-
+
- variable 2.5% 5% 95% 97.5%
-1 mu -1.441798 -0.1207198 13.16044 14.63687
-2 tau 0.822982 1.0590100 12.09968 13.78430
+ variable 2.5% 5% 95% 97.5%
+1 mu -1.614829 -0.119312 12.18247 13.54298
+2 tau 0.778481 0.778481 11.91315 13.43694
The summary functions are applied to the array of sample values, with
dimension iter_samplingxchains.
-
+
fit$summary(variables = NULL, dim, colMeans)
variable dim.1 dim.2 1 2 3 4
-1 lp__ 1000 4 -59.408601 -58.175641 -58.144473 -55.940741
-2 mu 1000 4 6.689529 6.327474 6.548562 6.121077
-3 tau 1000 4 6.015816 5.271415 5.222288 4.338039
-4 theta[1] 1000 4 10.309827 9.010128 9.089769 8.229576
-5 theta[2] 1000 4 6.940841 6.642238 6.917915 6.502333
-6 theta[3] 1000 4 5.378242 5.517793 5.881470 5.480988
-7 theta[4] 1000 4 6.732677 6.458448 6.658988 6.177763
-8 theta[5] 1000 4 4.460429 4.316417 4.822090 4.811620
-9 theta[6] 1000 4 5.567601 5.449356 5.743810 5.046207
-10 theta[7] 1000 4 9.872239 9.168575 9.143428 8.239549
-11 theta[8] 1000 4 6.985384 6.844136 7.146715 6.643711
+1 lp__ 1000 4 -56.698590 -58.592226 -56.911658 -50.122112
+2 mu 1000 4 6.836467 6.338743 6.214918 4.824501
+3 tau 1000 4 4.751132 5.540196 4.617587 2.916872
+4 theta[1] 1000 4 9.139180 9.470688 8.631066 6.506831
+5 theta[2] 1000 4 7.088238 6.756565 6.691301 5.555891
+6 theta[3] 1000 4 6.087235 5.430155 5.208329 3.864985
+7 theta[4] 1000 4 6.963234 6.883517 6.322756 4.588127
+8 theta[5] 1000 4 5.312623 4.530775 4.623091 4.212267
+9 theta[6] 1000 4 5.778286 4.999767 5.497782 4.216348
+10 theta[7] 1000 4 9.354323 9.367796 8.483796 5.926863
+11 theta[8] 1000 4 7.269928 7.165762 6.670007 4.893880
For this reason users may have unexpected results if they use
stats::var() directly, as it will return a covariance
matrix. An alternative is the distributional::variance()
function, which can also be accessed via
posterior::variance().
-
+
variable posterior::variance ~var(as.vector(.x))
-1 mu 15.95412 15.95412
-2 tau 11.95166 11.95166
+1 mu 14.26877 14.26877
+2 tau 13.26276 13.26276
Summary functions need not be numeric, but these won’t work with
$print().
-
+
strict_pos <- function(x) if (all(x > 0)) "yes" else "no"
fit$summary(variables = NULL, "Strictly Positive" = strict_pos)
variable Strictly Positive
@@ -283,7 +285,7 @@ Summary statistics
+
# fit$print(variables = NULL, "Strictly Positive" = strict_pos)
For more information, see posterior::summarise_draws(),
which is called by $summary().
@@ -297,49 +299,49 @@ Extracting posterior draws/samplesdraws_array
and
draws_df formats, but the posterior
package supports other useful formats as well.
-
+
# default is a 3-D draws_array object from the posterior package
# iterations x chains x variables
draws_arr <- fit$draws() # or format="array"
str(draws_arr)
- 'draws_array' num [1:1000, 1:4, 1:11] -57.1 -58.5 -56 -57.2 -58.5 ...
+ 'draws_array' num [1:1000, 1:4, 1:11] -61.7 -60.5 -61.8 -60.5 -66.8 ...
- attr(*, "dimnames")=List of 3
..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
..$ chain : chr [1:4] "1" "2" "3" "4"
..$ variable : chr [1:11] "lp__" "mu" "tau" "theta[1]" ...
-
+
# draws x variables data frame
draws_df <- fit$draws(format = "df")
str(draws_df)
draws_df [4,000 × 14] (S3: draws_df/draws/tbl_df/tbl/data.frame)
- $ lp__ : num [1:4000] -57.1 -58.5 -56 -57.2 -58.5 ...
- $ mu : num [1:4000] 9.17 4.62 11.15 5.67 5.37 ...
- $ tau : num [1:4000] 2.8 4.94 2.62 5.14 7.06 ...
- $ theta[1] : num [1:4000] 17.589 0.16 18.328 0.237 1.688 ...
- $ theta[2] : num [1:4000] 8.55 10.22 8.37 9.18 9.6 ...
- $ theta[3] : num [1:4000] 10.75 6.36 10.37 9.1 8.14 ...
- $ theta[4] : num [1:4000] 10.85 3.4 12.92 6.16 6.3 ...
- $ theta[5] : num [1:4000] 11 2.25 11.42 2.25 2.76 ...
- $ theta[6] : num [1:4000] 9.55 8.19 9.94 6.84 4.9 ...
- $ theta[7] : num [1:4000] 6.58 14.62 9.12 6.07 5.08 ...
- $ theta[8] : num [1:4000] 11.31 6.14 10.26 4.92 7.66 ...
+ $ lp__ : num [1:4000] -61.7 -60.5 -61.8 -60.5 -66.8 ...
+ $ mu : num [1:4000] 8.86 6.18 9.7 4.97 5.16 ...
+ $ tau : num [1:4000] 5.6 8.77 7.58 7.37 11.97 ...
+ $ theta[1] : num [1:4000] 16.716 14.062 6.67 6.25 -0.404 ...
+ $ theta[2] : num [1:4000] 4.377 6.173 15.608 0.638 10.449 ...
+ $ theta[3] : num [1:4000] 11.59 9.27 11.11 3.74 13.42 ...
+ $ theta[4] : num [1:4000] 13.68 12.51 7.77 6.48 10.16 ...
+ $ theta[5] : num [1:4000] -0.877 1.959 7.197 2.097 3.229 ...
+ $ theta[6] : num [1:4000] -5.02 -4.95 5.66 5.24 14.26 ...
+ $ theta[7] : num [1:4000] 13.38 17.44 13.69 6.65 13.77 ...
+ $ theta[8] : num [1:4000] 10.46 5.48 -6.15 20.92 31.79 ...
$ .chain : int [1:4000] 1 1 1 1 1 1 1 1 1 1 ...
$ .iteration: int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
$ .draw : int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
-
+
print(draws_df)
# A draws_df: 1000 iterations, 4 chains, and 11 variables
- lp__ mu tau theta[1] theta[2] theta[3] theta[4] theta[5]
-1 -57 9.17 2.8 17.59 8.5 10.75 10.8 11.0
-2 -58 4.62 4.9 0.16 10.2 6.36 3.4 2.3
-3 -56 11.15 2.6 18.33 8.4 10.37 12.9 11.4
-4 -57 5.67 5.1 0.24 9.2 9.10 6.2 2.3
-5 -59 5.37 7.1 1.69 9.6 8.14 6.3 2.8
-6 -59 0.64 4.4 6.20 6.6 0.44 4.1 2.4
-7 -53 7.85 3.0 6.58 6.7 8.34 7.4 6.7
-8 -54 8.05 3.9 7.66 6.5 4.54 7.1 8.3
-9 -54 6.80 3.3 7.75 8.5 1.37 6.6 9.4
-10 -59 5.09 5.8 0.40 9.4 -1.92 13.0 2.4
+ lp__ mu tau theta[1] theta[2] theta[3] theta[4] theta[5]
+1 -62 8.9 5.6 16.7 4.38 11.6 13.676 -0.88
+2 -61 6.2 8.8 14.1 6.17 9.3 12.515 1.96
+3 -62 9.7 7.6 6.7 15.61 11.1 7.770 7.20
+4 -60 5.0 7.4 6.3 0.64 3.7 6.477 2.10
+5 -67 5.2 12.0 -0.4 10.45 13.4 10.162 3.23
+6 -63 11.1 7.9 24.6 11.68 18.8 -1.606 3.79
+7 -61 14.7 6.1 28.9 11.15 15.3 6.780 9.95
+8 -63 13.4 5.7 13.7 21.03 7.2 20.051 12.99
+9 -64 16.0 8.3 20.2 2.65 11.3 19.000 3.51
+10 -59 2.9 4.5 4.2 11.37 2.5 -0.057 6.30
# ... with 3990 more draws, and 3 more variables
# ... hidden reserved variables {'.chain', '.iteration', '.draw'}
To convert an existing draws object to a different format use the
diff --git a/docs/articles/profiling.html b/docs/articles/profiling.html
index 613e6d86..dfbed9b1 100644
--- a/docs/articles/profiling.html
+++ b/docs/articles/profiling.html
@@ -250,11 +250,11 @@
Accessing the profiling info
fit$profiles()
[[1]]
name thread_id total_time forward_time reverse_time chain_stack
-1 likelihood 0x10cc54e00 0.76348000 0.62696000 0.13652000 51132
-2 priors 0x10cc54e00 0.00513667 0.00296958 0.00216709 34088
+1 likelihood 0x118759e00 0.69970800 0.57529800 0.12441000 51468
+2 priors 0x118759e00 0.00446571 0.00270154 0.00176417 34312
no_chain_stack autodiff_calls no_autodiff_calls
-1 34105044 17044 1
-2 34088 17044 1
+1 34329156 17156 1
+2 34312 17156 1
The total_time column is the total time spent inside a
given profile statement. It is clear that the vast majority of time is
spent in the likelihood function.
@@ -297,11 +297,11 @@ Comparing to a faster versio
fit_glm$profiles()
[[1]]
name thread_id total_time forward_time reverse_time chain_stack
-1 likelihood 0x115a87e00 0.39845200 0.39713500 0.00131726 49845
-2 priors 0x115a87e00 0.00381033 0.00234721 0.00146311 33230
+1 likelihood 0x106ce4e00 0.38400000 0.38264800 0.00135175 52203
+2 priors 0x106ce4e00 0.00356564 0.00237053 0.00119511 34802
no_chain_stack autodiff_calls no_autodiff_calls
-1 16615 16615 1
-2 33230 16615 1
+1 17401 17401 1
+2 34802 17401 1
We can see from the total_time column that this is much
faster than the previous model.
@@ -324,7 +324,7 @@ Per-gradient timings, and memory
profile_chain_1 <- fit$profiles()[[1]]
per_gradient_timing <- profile_chain_1$total_time/profile_chain_1$autodiff_calls
print(per_gradient_timing) # two elements for the two profile statements in the model
-[1] 4.479465e-05 3.013770e-07
+[1] 4.078503e-05 2.603002e-07
Accessing and saving the profile files
@@ -335,7 +335,7 @@ Accessing and saving the profile
$profile_files().
fit$profile_files()
-[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI4doTK/model_6580008f67848265f3dfd0e7ae3b0600-profile-202311081036-1-4a8018.csv"
+[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpJRtaoq/model_6580008f67848265f3dfd0e7ae3b0600-profile-202312131010-1-95e407.csv"
These can be saved to a more permanent location with the
$save_profile_files() method.
diff --git a/docs/index.html b/docs/index.html
index cc3f7698..1e06b71e 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -156,7 +156,7 @@ Goals
A clean interface to Stan services so that CmdStanR can keep up with Stan releases.
R code that doesn’t interface directly with C++, only calls compiled executables.
Modularity: CmdStanR runs Stan’s algorithms and lets downstream modules do the analysis.
Flexible BSD-3 license.
Flexible BSD-3 license.
License
CmdStanR, like CmdStan and the core Stan C++ code, is licensed under the following licenses:
-- Code: BSD 3-clause (https://opensource.org/licenses/BSD-3-Clause)
+- Code: BSD 3-clause (https://opensource.org/license/bsd-3-clause/)
- Documentation: CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
@@ -216,6 +216,12 @@ License
+
+Community
+
+
Citation
diff --git a/docs/news/index.html b/docs/news/index.html
index 64016e37..f702a044 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -123,6 +123,9 @@ Other improvements and bug fixes
Add support/tests for exposing functions with tuples by @andrjohns in #860
Add support/tests for exporting functions with complex types by @andrjohns in #861
Add option for installing from release archive by @andrjohns in #866
+Improve Pathfinder doc by @avehtari in #875
+Rename jacobian_adjustment argument to jacobian by @jgabry in #879
+Fix get_cmdstan_flags(‘STANCFLAGS’) in recursive make by @pearsonca in #881
@@ -312,7 +315,7 @@ Bug fixes
New features
-CSV reading is now faster by using data.table::fread(). (#318)
CSV reading is now faster by using data.table::fread(). (#318)
install_cmdstan() gains argument version for specifying which version of CmdStan to install. (#300, #308)
New function check_cmdstan_toolchain() that checks if the appropriate toolchains are available. (#289)
$sample() method for CmdStanModel objects gains argument chain_ids for specifying custom chain IDs. (#319)
Examples
# retrieve the gradients
test$gradients()
#> param_idx value model finite_diff error
-#> 1 0 0.797265 -8.06234 -8.06234 -9.72013e-09
-#> 2 1 0.216729 -15.64540 -15.64540 1.12721e-08
-#> 3 2 -0.207005 -4.61097 -4.61097 -5.25634e-09
-#> 4 3 1.118110 -6.60883 -6.60883 -2.24051e-08
+#> 1 0 -1.501560 34.56550 34.56550 4.58586e-08
+#> 2 1 -1.199130 1.69981 1.69981 -1.08299e-09
+#> 3 2 1.255420 -23.66640 -23.66640 -2.23140e-08
+#> 4 3 -0.452023 12.80740 12.80740 -8.57077e-09
# }
diff --git a/docs/reference/CmdStanGQ.html b/docs/reference/CmdStanGQ.html
index 28c7f7a4..6506d9af 100644
--- a/docs/reference/CmdStanGQ.html
+++ b/docs/reference/CmdStanGQ.html
@@ -177,7 +177,7 @@ Examples
#>
#> All 4 chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.6 seconds.
+#> Total execution time: 0.7 seconds.
#>
# stan program for standalone generated quantities
diff --git a/docs/reference/CmdStanModel.html b/docs/reference/CmdStanModel.html
index a309a128..6b2ba8d7 100644
--- a/docs/reference/CmdStanModel.html
+++ b/docs/reference/CmdStanModel.html
@@ -333,7 +333,7 @@ Examples
#> 6 -5.00402 0.000103557 2.55661e-07 1 1 9
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
-#> Finished in 0.1 seconds.
+#> Finished in 0.2 seconds.
fit_optim$summary()
#> # A tibble: 2 × 2
#> variable estimate
@@ -341,12 +341,12 @@ Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -6.94684
+#> Initial log joint probability = -6.92942
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 4 -6.74802 0.000319208 6.44211e-05 0.9206 0.9206 7
+#> 4 -6.74802 0.000269426 5.11368e-05 0.9249 0.9249 7
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -377,11 +377,11 @@ Examples
#> Finished in 0.1 seconds.
fit_laplace$summary()
#> # A tibble: 3 × 7
-#> variable mean median sd mad q5 q95
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.23 -6.96 0.692 0.298 -8.50 -6.75
-#> 2 lp_approx__ -0.490 -0.226 0.691 0.313 -1.88 -0.00180
-#> 3 theta 0.267 0.249 0.122 0.119 0.102 0.497
+#> variable mean median sd mad q5 q95
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -7.22 -6.97 0.652 0.296 -8.51 -6.75
+#> 2 lp_approx__ -0.492 -0.223 0.677 0.302 -1.91 -0.00222
+#> 3 theta 0.265 0.246 0.120 0.122 0.0952 0.484
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -450,6 +450,53 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -6.860971
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 7.999e-04 6.710e-06 1.000e+00 1.000e+00 101 -6.266e+00 -6.266e+00
+#> Path [1] :Best Iter: [2] ELBO (-6.159468) evaluations: (101)
+#> Path [2] :Initial log joint density = -15.050783
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.035e-03 6.058e-05 1.000e+00 1.000e+00 126 -6.231e+00 -6.231e+00
+#> Path [2] :Best Iter: [2] ELBO (-6.179167) evaluations: (126)
+#> Path [3] :Initial log joint density = -12.609181
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.519e-03 3.222e-05 1.000e+00 1.000e+00 126 -6.224e+00 -6.224e+00
+#> Path [3] :Best Iter: [3] ELBO (-6.177380) evaluations: (126)
+#> Path [4] :Initial log joint density = -7.551395
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.242e-04 5.362e-07 1.000e+00 1.000e+00 126 -6.267e+00 -6.267e+00
+#> Path [4] :Best Iter: [4] ELBO (-6.163367) evaluations: (126)
+#> Path [5] :Initial log joint density = -7.623161
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.410e-04 6.577e-07 1.000e+00 1.000e+00 126 -6.242e+00 -6.242e+00
+#> Path [5] :Best Iter: [5] ELBO (-6.241791) evaluations: (126)
+#> Path [6] :Initial log joint density = -12.604877
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.517e-03 3.214e-05 1.000e+00 1.000e+00 126 -6.220e+00 -6.220e+00
+#> Path [6] :Best Iter: [4] ELBO (-6.150438) evaluations: (126)
+#> Path [7] :Initial log joint density = -6.754804
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 1.079e-03 3.589e-05 1.000e+00 1.000e+00 76 -6.230e+00 -6.230e+00
+#> Path [7] :Best Iter: [2] ELBO (-6.228353) evaluations: (76)
+#> Path [8] :Initial log joint density = -6.839688
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 6.089e-04 4.257e-06 1.000e+00 1.000e+00 101 -6.279e+00 -6.279e+00
+#> Path [8] :Best Iter: [3] ELBO (-6.230942) evaluations: (101)
+#> Path [9] :Initial log joint density = -7.590990
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.335e-04 6.020e-07 1.000e+00 1.000e+00 126 -6.260e+00 -6.260e+00
+#> Path [9] :Best Iter: [4] ELBO (-6.210494) evaluations: (126)
+#> Path [10] :Initial log joint density = -11.878141
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.194e-03 2.066e-05 1.000e+00 1.000e+00 126 -6.255e+00 -6.255e+00
+#> Path [10] :Best Iter: [2] ELBO (-6.222876) evaluations: (126)
+#> Total log probability function evaluations:1310
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
@@ -516,7 +563,7 @@ Examples
#>
#> Both chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.4 seconds.
+#> Total execution time: 0.3 seconds.
#>
fit_optim_w_init_list <- mod$optimize(
data = stan_data,
diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png
index 6823093a..17a35806 100644
Binary files a/docs/reference/Rplot001.png and b/docs/reference/Rplot001.png differ
diff --git a/docs/reference/as_draws.CmdStanMCMC.html b/docs/reference/as_draws.CmdStanMCMC.html
index 40958e24..ab9c18ec 100644
--- a/docs/reference/as_draws.CmdStanMCMC.html
+++ b/docs/reference/as_draws.CmdStanMCMC.html
@@ -158,41 +158,41 @@ Examples
#>
#> chain
#> iteration 1 2 3 4
-#> 1 -66 -68 -66 -65
-#> 2 -65 -67 -65 -66
-#> 3 -68 -66 -65 -66
-#> 4 -68 -65 -65 -67
-#> 5 -67 -64 -65 -67
+#> 1 -66 -67 -65 -67
+#> 2 -65 -65 -64 -67
+#> 3 -66 -66 -65 -64
+#> 4 -65 -65 -65 -66
+#> 5 -67 -67 -66 -65
#>
#> , , variable = alpha
#>
#> chain
#> iteration 1 2 3 4
-#> 1 0.44 0.414 0.71 0.46
-#> 2 0.30 -0.045 0.44 0.17
-#> 3 0.65 0.397 0.14 0.55
-#> 4 0.29 0.644 0.47 0.54
-#> 5 0.53 0.435 0.48 0.20
+#> 1 0.37 0.0232 0.30 0.19
+#> 2 0.35 0.1359 0.50 0.56
+#> 3 0.39 0.0021 0.22 0.42
+#> 4 0.42 0.2264 0.22 0.21
+#> 5 0.30 0.6367 0.19 0.24
#>
#> , , variable = beta[1]
#>
#> chain
#> iteration 1 2 3 4
-#> 1 -0.91 -0.17 -0.50 -0.59
-#> 2 -0.57 -0.97 -0.78 -0.65
-#> 3 -0.90 -0.28 -0.38 -0.43
-#> 4 -1.20 -0.58 -0.92 -1.04
-#> 5 -0.59 -0.74 -0.70 -0.98
+#> 1 -0.21 -0.70 -0.87 -0.93
+#> 2 -0.42 -0.81 -0.58 -0.38
+#> 3 -1.01 -0.83 -0.81 -0.49
+#> 4 -0.95 -0.45 -0.67 -1.03
+#> 5 -0.16 -0.96 -0.73 -0.89
#>
#> , , variable = beta[2]
#>
#> chain
-#> iteration 1 2 3 4
-#> 1 0.088 -0.192 -0.43 -0.621
-#> 2 -0.515 -0.088 -0.35 -0.591
-#> 3 -0.031 -0.444 -0.11 0.017
-#> 4 -0.653 -0.229 -0.37 0.066
-#> 5 -0.345 -0.277 -0.13 -0.678
+#> iteration 1 2 3 4
+#> 1 -0.3841 0.08 -0.574 -0.73
+#> 2 -0.2843 -0.27 -0.376 0.12
+#> 3 -0.0223 -0.13 -0.065 -0.11
+#> 4 0.0016 -0.28 -0.100 -0.29
+#> 5 -0.5327 -0.66 -0.644 -0.16
#>
#> # ... with 995 more iterations, and 101 more variables
@@ -200,70 +200,71 @@ Examples
posterior::as_draws_rvars(fit)
#> # A draws_rvars: 1000 iterations, 4 chains, and 4 variables
#> $lp__: rvar<1000,4>[1] mean ± sd:
-#> [1] -66 ± 1.5
+#> [1] -66 ± 1.4
#>
#> $alpha: rvar<1000,4>[1] mean ± sd:
-#> [1] 0.38 ± 0.22
+#> [1] 0.37 ± 0.22
#>
#> $beta: rvar<1000,4>[3] mean ± sd:
-#> [1] -0.67 ± 0.26 -0.27 ± 0.23 0.69 ± 0.27
+#> [1] -0.66 ± 0.25 -0.27 ± 0.23 0.67 ± 0.26
#>
#> $log_lik: rvar<1000,4>[100] mean ± sd:
-#> [1] -0.517 ± 0.098 -0.401 ± 0.152 -0.501 ± 0.223 -0.448 ± 0.154
-#> [5] -1.185 ± 0.289 -0.590 ± 0.195 -0.640 ± 0.126 -0.278 ± 0.135
-#> [9] -0.693 ± 0.170 -0.742 ± 0.238 -0.279 ± 0.127 -0.494 ± 0.244
-#> [13] -0.659 ± 0.215 -0.363 ± 0.176 -0.279 ± 0.108 -0.275 ± 0.087
-#> [17] -1.593 ± 0.288 -0.479 ± 0.107 -0.232 ± 0.076 -0.113 ± 0.080
-#> [21] -0.211 ± 0.089 -0.568 ± 0.149 -0.330 ± 0.140 -0.136 ± 0.067
-#> [25] -0.454 ± 0.123 -1.517 ± 0.342 -0.306 ± 0.123 -0.445 ± 0.083
-#> [29] -0.725 ± 0.232 -0.696 ± 0.191 -0.487 ± 0.164 -0.423 ± 0.109
-#> [33] -0.409 ± 0.130 -0.064 ± 0.052 -0.587 ± 0.189 -0.325 ± 0.132
-#> [37] -0.700 ± 0.231 -0.312 ± 0.151 -0.180 ± 0.111 -0.681 ± 0.128
-#> [41] -1.133 ± 0.258 -0.932 ± 0.200 -0.416 ± 0.265 -1.175 ± 0.185
-#> [45] -0.360 ± 0.118 -0.580 ± 0.131 -0.303 ± 0.131 -0.324 ± 0.082
-#> [49] -0.319 ± 0.079 -1.285 ± 0.333 -0.289 ± 0.094 -0.832 ± 0.142
-#> [53] -0.401 ± 0.131 -0.373 ± 0.143 -0.384 ± 0.139 -0.321 ± 0.192
-#> [57] -0.658 ± 0.119 -0.951 ± 0.363 -1.364 ± 0.348 -0.976 ± 0.158
-#> [61] -0.542 ± 0.097 -0.871 ± 0.318 -0.116 ± 0.075 -0.901 ± 0.250
-#> [65] -2.026 ± 0.610 -0.508 ± 0.138 -0.276 ± 0.082 -1.061 ± 0.237
-#> [69] -0.435 ± 0.083 -0.641 ± 0.235 -0.613 ± 0.214 -0.464 ± 0.177
-#> [73] -1.489 ± 0.374 -0.948 ± 0.198 -1.136 ± 0.394 -0.374 ± 0.140
-#> [77] -0.878 ± 0.139 -0.491 ± 0.174 -0.765 ± 0.194 -0.539 ± 0.200
-#> [81] -0.163 ± 0.102 -0.223 ± 0.145 -0.343 ± 0.080 -0.275 ± 0.092
-#> [85] -0.130 ± 0.075 -1.132 ± 0.322 -0.822 ± 0.126 -0.780 ± 0.249
-#> [89] -1.283 ± 0.323 -0.261 ± 0.137 -0.387 ± 0.133 -1.501 ± 0.352
-#> [93] -0.737 ± 0.221 -0.318 ± 0.087 -0.388 ± 0.112 -1.573 ± 0.284
-#> [97] -0.432 ± 0.100 -1.053 ± 0.383 -0.693 ± 0.143 -0.392 ± 0.096
+#> [1] -0.518 ± 0.099 -0.405 ± 0.147 -0.501 ± 0.220 -0.451 ± 0.153
+#> [5] -1.173 ± 0.276 -0.592 ± 0.185 -0.640 ± 0.123 -0.280 ± 0.135
+#> [9] -0.693 ± 0.163 -0.740 ± 0.234 -0.283 ± 0.124 -0.500 ± 0.241
+#> [13] -0.655 ± 0.206 -0.364 ± 0.175 -0.282 ± 0.106 -0.278 ± 0.087
+#> [17] -1.582 ± 0.288 -0.481 ± 0.107 -0.235 ± 0.076 -0.115 ± 0.079
+#> [21] -0.215 ± 0.088 -0.570 ± 0.144 -0.332 ± 0.140 -0.139 ± 0.067
+#> [25] -0.456 ± 0.121 -1.513 ± 0.345 -0.309 ± 0.120 -0.447 ± 0.083
+#> [29] -0.722 ± 0.227 -0.694 ± 0.189 -0.489 ± 0.157 -0.427 ± 0.106
+#> [33] -0.412 ± 0.126 -0.065 ± 0.052 -0.585 ± 0.184 -0.327 ± 0.135
+#> [37] -0.700 ± 0.227 -0.314 ± 0.150 -0.182 ± 0.110 -0.681 ± 0.126
+#> [41] -1.124 ± 0.248 -0.928 ± 0.195 -0.412 ± 0.267 -1.169 ± 0.185
+#> [45] -0.362 ± 0.119 -0.583 ± 0.128 -0.307 ± 0.129 -0.327 ± 0.084
+#> [49] -0.322 ± 0.079 -1.286 ± 0.334 -0.291 ± 0.096 -0.833 ± 0.144
+#> [53] -0.404 ± 0.127 -0.373 ± 0.143 -0.388 ± 0.134 -0.322 ± 0.195
+#> [57] -0.658 ± 0.117 -0.951 ± 0.353 -1.351 ± 0.337 -0.975 ± 0.159
+#> [61] -0.542 ± 0.099 -0.876 ± 0.314 -0.119 ± 0.075 -0.901 ± 0.247
+#> [65] -1.995 ± 0.581 -0.511 ± 0.134 -0.279 ± 0.083 -1.057 ± 0.233
+#> [69] -0.438 ± 0.083 -0.638 ± 0.235 -0.609 ± 0.207 -0.463 ± 0.169
+#> [73] -1.476 ± 0.363 -0.944 ± 0.194 -1.139 ± 0.389 -0.376 ± 0.140
+#> [77] -0.875 ± 0.136 -0.489 ± 0.174 -0.764 ± 0.190 -0.545 ± 0.196
+#> [81] -0.165 ± 0.100 -0.226 ± 0.138 -0.346 ± 0.081 -0.278 ± 0.091
+#> [85] -0.132 ± 0.075 -1.120 ± 0.315 -0.822 ± 0.127 -0.773 ± 0.237
+#> [89] -1.270 ± 0.314 -0.263 ± 0.136 -0.388 ± 0.129 -1.484 ± 0.335
+#> [93] -0.738 ± 0.218 -0.321 ± 0.088 -0.391 ± 0.110 -1.562 ± 0.280
+#> [97] -0.433 ± 0.102 -1.052 ± 0.372 -0.693 ± 0.139 -0.393 ± 0.098
#>
posterior::as_draws_list(fit)
#> # A draws_list: 1000 iterations, 4 chains, and 105 variables
#>
#> [chain = 1]
#> $lp__
-#> [1] -66 -65 -68 -68 -67 -66 -65 -66 -67 -66
+#> [1] -66 -65 -66 -65 -67 -65 -65 -64 -67 -69
#>
#> $alpha
-#> [1] 0.44 0.30 0.65 0.29 0.53 0.53 0.47 0.38 0.32 0.51
+#> [1] 0.373 0.355 0.389 0.419 0.300 0.178 0.629 0.288 0.016 -0.075
#>
#> $`beta[1]`
-#> [1] -0.91 -0.57 -0.90 -1.20 -0.59 -0.51 -0.36 -0.19 -1.17 -1.02
+#> [1] -0.21 -0.42 -1.01 -0.95 -0.16 -0.51 -0.69 -0.67 -0.74 -0.71
#>
#> $`beta[2]`
-#> [1] 0.088 -0.515 -0.031 -0.653 -0.345 -0.646 -0.154 -0.488 -0.013 0.034
+#> [1] -0.3841 -0.2843 -0.0223 0.0016 -0.5327 -0.3040 -0.3392 -0.2792 0.0976
+#> [10] 0.1916
#>
#>
#> [chain = 2]
#> $lp__
-#> [1] -68 -67 -66 -65 -64 -65 -65 -64 -65 -65
+#> [1] -67 -65 -66 -65 -67 -67 -67 -64 -68 -66
#>
#> $alpha
-#> [1] 0.414 -0.045 0.397 0.644 0.435 0.266 0.415 0.369 0.229 0.185
+#> [1] 0.0232 0.1359 0.0021 0.2264 0.6367 0.7152 0.4707 0.3940 0.0634 0.4544
#>
#> $`beta[1]`
-#> [1] -0.17 -0.97 -0.28 -0.58 -0.74 -0.48 -0.83 -0.72 -0.44 -0.58
+#> [1] -0.70 -0.81 -0.83 -0.45 -0.96 -0.34 -1.17 -0.57 -0.62 -0.62
#>
#> $`beta[2]`
-#> [1] -0.192 -0.088 -0.444 -0.229 -0.277 -0.314 0.046 -0.240 -0.080 -0.090
+#> [1] 0.08 -0.27 -0.13 -0.28 -0.66 -0.57 -0.21 -0.20 -0.47 -0.67
#>
#> # ... with 990 more iterations, and 2 more chains, and 101 more variables
# }
diff --git a/docs/reference/cmdstan_model.html b/docs/reference/cmdstan_model.html
index 9385aa48..a94e571b 100644
--- a/docs/reference/cmdstan_model.html
+++ b/docs/reference/cmdstan_model.html
@@ -358,12 +358,12 @@ Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -6.79903
+#> Initial log joint probability = -17.5452
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 3 -6.74802 0.00379849 4.77236e-05 0.9516 0.9516 6
+#> 5 -6.74802 0.00115356 2.90728e-05 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -394,11 +394,11 @@ Examples
#> Finished in 0.1 seconds.
fit_laplace$summary()
#> # A tibble: 3 × 7
-#> variable mean median sd mad q5 q95
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.22 -6.97 0.659 0.305 -8.52 -6.75
-#> 2 lp_approx__ -0.481 -0.224 0.662 0.308 -1.84 -0.00186
-#> 3 theta 0.272 0.252 0.121 0.122 0.103 0.502
+#> variable mean median sd mad q5 q95
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -7.27 -7.00 0.705 0.338 -8.72 -6.75
+#> 2 lp_approx__ -0.527 -0.251 0.714 0.340 -2.02 -0.00142
+#> 3 theta 0.270 0.251 0.126 0.125 0.0959 0.509
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -407,8 +407,8 @@ Examples
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
-#> Gradient evaluation took 6e-06 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
+#> Gradient evaluation took 5e-06 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
@@ -467,6 +467,53 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -11.515672
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.064e-03 1.692e-05 1.000e+00 1.000e+00 126 -6.254e+00 -6.254e+00
+#> Path [1] :Best Iter: [3] ELBO (-6.213658) evaluations: (126)
+#> Path [2] :Initial log joint density = -9.189304
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 3.749e-04 3.327e-06 1.000e+00 1.000e+00 126 -6.289e+00 -6.289e+00
+#> Path [2] :Best Iter: [4] ELBO (-6.214534) evaluations: (126)
+#> Path [3] :Initial log joint density = -13.270061
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.767e-03 4.305e-05 1.000e+00 1.000e+00 126 -6.276e+00 -6.276e+00
+#> Path [3] :Best Iter: [3] ELBO (-6.225363) evaluations: (126)
+#> Path [4] :Initial log joint density = -6.748213
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 2 -6.748e+00 1.004e-02 7.315e-05 1.000e+00 1.000e+00 51 -6.200e+00 -6.200e+00
+#> Path [4] :Best Iter: [2] ELBO (-6.200114) evaluations: (51)
+#> Path [5] :Initial log joint density = -7.368682
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 5.377e-04 9.870e-07 1.000e+00 1.000e+00 126 -6.276e+00 -6.276e+00
+#> Path [5] :Best Iter: [3] ELBO (-6.167297) evaluations: (126)
+#> Path [6] :Initial log joint density = -9.465368
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 4.613e-04 4.599e-06 1.000e+00 1.000e+00 126 -6.238e+00 -6.238e+00
+#> Path [6] :Best Iter: [3] ELBO (-6.185454) evaluations: (126)
+#> Path [7] :Initial log joint density = -6.856712
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 1.028e-04 1.386e-05 9.452e-01 9.452e-01 101 -6.242e+00 -6.242e+00
+#> Path [7] :Best Iter: [3] ELBO (-6.159687) evaluations: (101)
+#> Path [8] :Initial log joint density = -7.085030
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 3.058e-03 6.268e-05 1.000e+00 1.000e+00 101 -6.221e+00 -6.221e+00
+#> Path [8] :Best Iter: [4] ELBO (-6.221460) evaluations: (101)
+#> Path [9] :Initial log joint density = -7.443992
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 9.951e-05 3.747e-07 1.000e+00 1.000e+00 126 -6.228e+00 -6.228e+00
+#> Path [9] :Best Iter: [2] ELBO (-6.206717) evaluations: (126)
+#> Path [10] :Initial log joint density = -6.751684
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 5.950e-04 1.461e-05 1.000e+00 1.000e+00 76 -6.209e+00 -6.209e+00
+#> Path [10] :Best Iter: [3] ELBO (-6.209430) evaluations: (76)
+#> Total log probability function evaluations:1235
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
diff --git a/docs/reference/cmdstanr-package.html b/docs/reference/cmdstanr-package.html
index 6e29914b..072311bb 100644
--- a/docs/reference/cmdstanr-package.html
+++ b/docs/reference/cmdstanr-package.html
@@ -291,7 +291,7 @@ Examples
#> [1] 0 0
#>
#> $ebfmi
-#> [1] 1.0 1.3
+#> [1] 1.017555 1.250490
#>
# Get posterior draws
@@ -364,12 +364,12 @@ Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -7.20447
+#> Initial log joint probability = -18.9282
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 4 -6.74802 0.00428356 0.000109727 1 1 7
+#> 5 -6.74802 0.00032939 4.62604e-06 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -397,14 +397,14 @@ Examples
#> iteration: 1700
#> iteration: 1800
#> iteration: 1900
-#> Finished in 0.2 seconds.
+#> Finished in 0.1 seconds.
fit_laplace$summary()
#> # A tibble: 3 × 7
#> variable mean median sd mad q5 q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.23 -6.96 0.694 0.287 -8.53 -6.75
-#> 2 lp_approx__ -0.493 -0.211 0.714 0.286 -1.87 -0.00222
-#> 3 theta 0.264 0.248 0.120 0.120 0.0954 0.491
+#> 1 lp__ -7.23 -6.97 0.683 0.304 -8.57 -6.75
+#> 2 lp_approx__ -0.496 -0.225 0.743 0.305 -1.91 -0.00217
+#> 3 theta 0.264 0.247 0.120 0.119 0.0967 0.492
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -473,6 +473,53 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -12.637372
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.531e-03 3.268e-05 1.000e+00 1.000e+00 126 -6.214e+00 -6.214e+00
+#> Path [1] :Best Iter: [5] ELBO (-6.213940) evaluations: (126)
+#> Path [2] :Initial log joint density = -11.196746
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 9.877e-04 1.507e-05 1.000e+00 1.000e+00 126 -6.283e+00 -6.283e+00
+#> Path [2] :Best Iter: [4] ELBO (-6.223987) evaluations: (126)
+#> Path [3] :Initial log joint density = -10.337120
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 7.410e-04 9.634e-06 1.000e+00 1.000e+00 126 -6.236e+00 -6.236e+00
+#> Path [3] :Best Iter: [2] ELBO (-6.179583) evaluations: (126)
+#> Path [4] :Initial log joint density = -12.005851
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.253e-03 2.257e-05 1.000e+00 1.000e+00 126 -6.159e+00 -6.159e+00
+#> Path [4] :Best Iter: [5] ELBO (-6.159209) evaluations: (126)
+#> Path [5] :Initial log joint density = -11.377707
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.032e-03 1.614e-05 1.000e+00 1.000e+00 126 -6.206e+00 -6.206e+00
+#> Path [5] :Best Iter: [2] ELBO (-6.202552) evaluations: (126)
+#> Path [6] :Initial log joint density = -7.049866
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 2.694e-03 5.075e-05 1.000e+00 1.000e+00 101 -6.203e+00 -6.203e+00
+#> Path [6] :Best Iter: [4] ELBO (-6.203218) evaluations: (101)
+#> Path [7] :Initial log joint density = -9.219130
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 3.841e-04 3.455e-06 1.000e+00 1.000e+00 126 -6.185e+00 -6.185e+00
+#> Path [7] :Best Iter: [5] ELBO (-6.185156) evaluations: (126)
+#> Path [8] :Initial log joint density = -17.192426
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.354e-03 3.651e-05 1.000e+00 1.000e+00 126 -6.204e+00 -6.204e+00
+#> Path [8] :Best Iter: [4] ELBO (-6.168586) evaluations: (126)
+#> Path [9] :Initial log joint density = -9.491732
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 4.697e-04 4.731e-06 1.000e+00 1.000e+00 126 -6.247e+00 -6.247e+00
+#> Path [9] :Best Iter: [3] ELBO (-6.220590) evaluations: (126)
+#> Path [10] :Initial log joint density = -6.750036
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 3.322e-04 6.069e-06 1.000e+00 1.000e+00 76 -6.214e+00 -6.214e+00
+#> Path [10] :Best Iter: [2] ELBO (-6.188430) evaluations: (76)
+#> Total log probability function evaluations:1335
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
@@ -488,7 +535,7 @@ Examples
#>
#> Both chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.7 seconds.
+#> Total execution time: 0.3 seconds.
#>
fit_mcmc_w_init_fun_2 <- mod$sample(
data = stan_data,
@@ -517,7 +564,7 @@ Examples
#>
#> [[2]]
#> [[2]]$theta
-#> [1] 0.33
+#> [1] 0.3333333
#>
#>
@@ -539,7 +586,7 @@ Examples
#>
#> Both chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.5 seconds.
+#> Total execution time: 0.3 seconds.
#>
fit_optim_w_init_list <- mod$optimize(
data = stan_data,
diff --git a/docs/reference/cmdstanr_example.html b/docs/reference/cmdstanr_example.html
index babf2ce0..8d377b8f 100644
--- a/docs/reference/cmdstanr_example.html
+++ b/docs/reference/cmdstanr_example.html
@@ -187,18 +187,18 @@ Examples
fit_logistic_mcmc <- cmdstanr_example("logistic", chains = 2)
fit_logistic_mcmc$summary()
#> # A tibble: 105 × 10
-#> variable mean median sd mad q5 q95 rhat ess_bulk
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -66.1 -65.7 1.53 1.26 -69.1 -64.3 1.00 1090.
-#> 2 alpha 0.373 0.371 0.223 0.215 0.0169 0.729 1.00 1944.
-#> 3 beta[1] -0.678 -0.676 0.260 0.261 -1.11 -0.265 1.00 1936.
-#> 4 beta[2] -0.276 -0.274 0.234 0.239 -0.674 0.0978 1.00 1841.
-#> 5 beta[3] 0.684 0.681 0.268 0.270 0.270 1.13 1.00 2070.
-#> 6 log_lik[1] -0.518 -0.509 0.102 0.101 -0.694 -0.366 0.999 1983.
-#> 7 log_lik[2] -0.398 -0.380 0.149 0.143 -0.665 -0.194 1.00 2108.
-#> 8 log_lik[3] -0.501 -0.461 0.228 0.210 -0.939 -0.204 1.00 1995.
-#> 9 log_lik[4] -0.446 -0.428 0.157 0.148 -0.740 -0.226 1.00 1806.
-#> 10 log_lik[5] -1.18 -1.15 0.285 0.286 -1.67 -0.754 1.00 2253.
+#> variable mean median sd mad q5 q95 rhat ess_bulk
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -66.0 -65.6 1.45 1.20 -68.7 -64.3 1.00 934.
+#> 2 alpha 0.384 0.386 0.218 0.218 0.0341 0.748 1.00 1959.
+#> 3 beta[1] -0.667 -0.661 0.247 0.248 -1.08 -0.273 1.00 1840.
+#> 4 beta[2] -0.275 -0.273 0.231 0.231 -0.662 0.114 1.00 1954.
+#> 5 beta[3] 0.687 0.685 0.271 0.269 0.254 1.13 1.00 2212.
+#> 6 log_lik[1] -0.515 -0.509 0.101 0.0967 -0.698 -0.360 1.00 2038.
+#> 7 log_lik[2] -0.402 -0.380 0.150 0.140 -0.673 -0.195 1.00 2565.
+#> 8 log_lik[3] -0.498 -0.467 0.223 0.212 -0.922 -0.201 1.00 2174.
+#> 9 log_lik[4] -0.449 -0.430 0.154 0.148 -0.731 -0.232 0.999 1785.
+#> 10 log_lik[5] -1.19 -1.17 0.283 0.281 -1.69 -0.772 1.00 2119.
#> # ℹ 95 more rows
#> # ℹ 1 more variable: ess_tail <dbl>
@@ -211,7 +211,7 @@ Examples
#> 2 alpha 0.364
#> 3 beta[1] -0.632
#> 4 beta[2] -0.259
-#> 5 beta[3] 0.649
+#> 5 beta[3] 0.648
#> 6 log_lik[1] -0.515
#> 7 log_lik[2] -0.394
#> 8 log_lik[3] -0.469
@@ -222,18 +222,18 @@ Examples
fit_logistic_vb <- cmdstanr_example("logistic", method = "variational")
fit_logistic_vb$summary()
#> # A tibble: 106 × 7
-#> variable mean median sd mad q5 q95
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -66.5 -66.1 1.98 1.74 -70.5 -64.4
-#> 2 lp_approx__ -2.01 -1.63 1.53 1.27 -5.07 -0.341
-#> 3 alpha 0.305 0.311 0.248 0.234 -0.116 0.716
-#> 4 beta[1] -0.623 -0.633 0.230 0.239 -0.987 -0.255
-#> 5 beta[2] -0.377 -0.369 0.263 0.258 -0.821 0.0339
-#> 6 beta[3] 0.681 0.671 0.291 0.288 0.215 1.16
-#> 7 log_lik[1] -0.565 -0.558 0.118 0.108 -0.766 -0.383
-#> 8 log_lik[2] -0.412 -0.388 0.172 0.156 -0.740 -0.186
-#> 9 log_lik[3] -0.641 -0.589 0.294 0.280 -1.22 -0.265
-#> 10 log_lik[4] -0.389 -0.367 0.145 0.134 -0.662 -0.190
+#> variable mean median sd mad q5 q95
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -66.5 -66.0 1.82 1.62 -69.9 -64.3
+#> 2 lp_approx__ -1.96 -1.62 1.41 1.19 -4.62 -0.318
+#> 3 alpha 0.503 0.492 0.219 0.222 0.144 0.876
+#> 4 beta[1] -0.582 -0.568 0.301 0.288 -1.10 -0.0916
+#> 5 beta[2] -0.278 -0.275 0.185 0.183 -0.592 0.0118
+#> 6 beta[3] 0.688 0.696 0.301 0.307 0.211 1.18
+#> 7 log_lik[1] -0.480 -0.475 0.0981 0.0994 -0.648 -0.332
+#> 8 log_lik[2] -0.466 -0.443 0.190 0.185 -0.836 -0.197
+#> 9 log_lik[3] -0.470 -0.443 0.195 0.188 -0.838 -0.209
+#> 10 log_lik[4] -0.495 -0.480 0.152 0.148 -0.776 -0.277
#> # ℹ 96 more rows
print_example_program("schools")
@@ -254,23 +254,23 @@ Examples
#> target += normal_lpdf(y | theta, sigma);
#> }
fit_schools_mcmc <- cmdstanr_example("schools")
-#> Warning: 91 of 4000 (2.0%) transitions ended with a divergence.
+#> Warning: 146 of 4000 (4.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.
fit_schools_mcmc$summary()
#> # A tibble: 11 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -57.8 -58.2 5.50 5.71 -66.3 -48.3 1.04 151. 248.
-#> 2 mu 6.47 6.50 4.26 4.17 -1.05 13.3 1.01 464. 141.
-#> 3 tau 5.22 4.49 3.59 3.40 0.984 12.2 1.04 115. 122.
-#> 4 theta[1] 9.21 8.56 7.05 6.14 -1.40 21.8 1.01 427. 239.
-#> 5 theta[2] 6.77 6.75 5.62 5.20 -2.79 15.9 1.01 718. 443.
-#> 6 theta[3] 5.35 5.83 6.82 5.87 -6.13 15.5 1.01 1103. 1908.
-#> 7 theta[4] 6.84 6.85 5.86 5.44 -2.42 16.6 1.01 923. 2076.
-#> 8 theta[5] 4.64 4.91 5.62 5.40 -4.88 13.4 1.01 775. 1603.
-#> 9 theta[6] 5.51 5.70 6.05 5.48 -4.39 15.1 1.01 639. 1878.
-#> 10 theta[7] 8.94 8.42 6.14 5.47 -0.805 19.6 1.02 272. 164.
-#> 11 theta[8] 7.08 6.94 6.75 5.70 -3.52 18.0 1.01 969. 1764.
+#> 1 lp__ -58.4 -58.5 5.06 5.42 -66.4 -50.2 1.02 139. 71.0
+#> 2 mu 6.36 6.39 4.24 4.27 -0.684 13.0 1.01 487. 345.
+#> 3 tau 5.42 4.61 3.53 3.33 1.25 12.3 1.02 136. 67.5
+#> 4 theta[1] 9.11 8.41 7.20 6.29 -1.35 21.8 1.01 774. 601.
+#> 5 theta[2] 6.65 6.69 5.88 5.59 -2.86 16.2 1.01 1011. 2093.
+#> 6 theta[3] 5.38 5.61 6.74 6.18 -5.83 15.9 1.01 863. 1953.
+#> 7 theta[4] 6.59 6.59 6.01 5.73 -2.94 16.6 1.01 946. 2109.
+#> 8 theta[5] 4.37 4.72 5.66 5.51 -5.63 12.8 1.01 745. 2276.
+#> 9 theta[6] 5.28 5.49 5.96 5.60 -4.99 14.6 1.01 879. 1751.
+#> 10 theta[7] 9.06 8.65 6.17 5.72 -0.151 20.1 1.01 678. 1072.
+#> 11 theta[8] 6.91 6.84 6.86 6.09 -4.10 17.8 1.01 939. 2286.
print_example_program("schools_ncp")
#> data {
@@ -295,36 +295,35 @@ Examples
fit_schools_ncp_mcmc <- cmdstanr_example("schools_ncp")
fit_schools_ncp_mcmc$summary()
#> # A tibble: 19 × 10
-#> variable mean median sd mad q5 q95 rhat ess_bulk
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -46.9 -46.6 2.47 2.39 -51.5 -43.5 1.00 1443.
-#> 2 mu 6.50 6.57 4.25 4.13 -0.446 13.4 1.00 2941.
-#> 3 tau 4.78 3.91 3.74 3.46 0.374 12.1 1.00 1743.
-#> 4 theta_raw[1] 0.372 0.381 0.989 0.951 -1.28 1.97 1.00 3537.
-#> 5 theta_raw[2] 0.0565 0.0620 0.915 0.928 -1.45 1.53 1.00 4450.
-#> 6 theta_raw[3] -0.148 -0.163 0.938 0.932 -1.65 1.40 1.00 3407.
-#> 7 theta_raw[4] 0.00702 0.00718 0.937 0.912 -1.55 1.53 1.00 3544.
-#> 8 theta_raw[5] -0.264 -0.271 0.896 0.870 -1.70 1.23 1.00 3495.
-#> 9 theta_raw[6] -0.148 -0.157 0.950 0.938 -1.68 1.43 1.00 4517.
-#> 10 theta_raw[7] 0.351 0.374 0.924 0.876 -1.23 1.82 1.00 3596.
-#> 11 theta_raw[8] 0.0685 0.0540 0.960 0.970 -1.52 1.64 1.00 3657.
-#> 12 theta[1] 8.96 8.30 6.79 5.56 -0.975 21.5 1.00 3374.
-#> 13 theta[2] 6.90 6.72 5.56 5.25 -2.07 16.0 1.00 4663.
-#> 14 theta[3] 5.58 5.91 6.35 5.42 -5.10 15.2 1.00 3392.
-#> 15 theta[4] 6.67 6.72 6.02 5.40 -2.96 16.2 1.00 3971.
-#> 16 theta[5] 4.81 5.19 5.50 5.21 -4.81 12.9 1.00 4119.
-#> 17 theta[6] 5.55 5.86 5.93 5.29 -4.38 14.7 1.00 4010.
-#> 18 theta[7] 8.73 8.24 5.88 5.28 -0.0503 19.3 1.00 3955.
-#> 19 theta[8] 7.01 6.84 6.39 5.49 -3.28 17.9 1.00 3861.
-#> # ℹ 1 more variable: ess_tail <dbl>
+#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -46.9 -4.66e+1 2.49 2.36 -51.6 -43.6 1.00 1229. 1844.
+#> 2 mu 6.43 6.46e+0 4.08 3.93 -0.238 13.1 1.00 2985. 2197.
+#> 3 tau 4.74 4.03e+0 3.70 3.41 0.334 11.9 1.00 1724. 1496.
+#> 4 theta_r… 0.351 3.53e-1 0.979 0.955 -1.26 1.96 1.00 3404. 2385.
+#> 5 theta_r… 0.0484 6.67e-2 0.924 0.913 -1.49 1.53 1.00 3680. 2435.
+#> 6 theta_r… -0.131 -1.50e-1 0.962 0.938 -1.71 1.50 1.00 3067. 2557.
+#> 7 theta_r… 0.0221 5.18e-3 0.919 0.905 -1.49 1.59 1.00 3748. 2583.
+#> 8 theta_r… -0.273 -3.14e-1 0.931 0.892 -1.77 1.34 1.00 4139. 2891.
+#> 9 theta_r… -0.157 -1.57e-1 0.930 0.929 -1.69 1.36 1.00 3560. 2797.
+#> 10 theta_r… 0.363 4.01e-1 0.924 0.911 -1.15 1.87 1.00 3687. 2941.
+#> 11 theta_r… 0.0558 7.25e-2 0.951 0.924 -1.55 1.65 1.00 4077. 2555.
+#> 12 theta[1] 8.85 8.09e+0 6.85 5.77 -0.638 21.2 1.00 2994. 2531.
+#> 13 theta[2] 6.79 6.75e+0 5.59 5.01 -2.46 15.9 1.00 4593. 3367.
+#> 14 theta[3] 5.40 5.88e+0 6.64 5.63 -5.94 15.5 1.00 3448. 3181.
+#> 15 theta[4] 6.65 6.62e+0 5.69 5.09 -2.25 16.0 1.00 4537. 3155.
+#> 16 theta[5] 4.78 5.06e+0 5.54 5.26 -4.69 13.2 1.00 3996. 3191.
+#> 17 theta[6] 5.44 5.63e+0 5.97 5.29 -4.66 14.8 1.00 3934. 2877.
+#> 18 theta[7] 8.83 8.37e+0 5.98 5.56 0.141 19.4 1.00 4104. 3336.
+#> 19 theta[8] 6.89 6.73e+0 6.34 5.33 -3.03 17.1 1.00 3988. 3025.
# optimization fails for hierarchical model
cmdstanr_example("schools", "optimize", quiet = FALSE)
-#> Initial log joint probability = -319.937
+#> Initial log joint probability = -56.2449
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 99 120.913 0.248985 3.76333e+09 1 1 183
+#> 99 121.706 0.109668 2.48002e+09 1 1 174
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 180 242.851 0.0892321 2.24452e+16 1e-12 0.001 378 LS failed, Hessian reset
+#> 187 244.785 0.253817 8.00296 1e-12 0.001 400 LS failed, Hessian reset
#> Line search failed to achieve a sufficient decrease, no more progress can be made
#> Chain 1 Optimization terminated with error:
#> Warning: Fitting finished unexpectedly! Use the $output() method for more information.
diff --git a/docs/reference/draws_to_csv.html b/docs/reference/draws_to_csv.html
index 9f523602..355224d7 100644
--- a/docs/reference/draws_to_csv.html
+++ b/docs/reference/draws_to_csv.html
@@ -169,10 +169,10 @@ Examples
draws_csv_files <- draws_to_csv(draws)
print(draws_csv_files)
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-1-7ea6a2.csv"
-#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-2-7ea6a2.csv"
-#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-3-7ea6a2.csv"
-#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-4-7ea6a2.csv"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpAMFHpW/fittedParams-202312131001-1-1dd4e6.csv"
+#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpAMFHpW/fittedParams-202312131001-2-1dd4e6.csv"
+#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpAMFHpW/fittedParams-202312131001-3-1dd4e6.csv"
+#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpAMFHpW/fittedParams-202312131001-4-1dd4e6.csv"
# draws_csv_files <- draws_to_csv(draws,
# sampler_diagnostic = sampler_diagnostics,
diff --git a/docs/reference/fit-method-cmdstan_summary.html b/docs/reference/fit-method-cmdstan_summary.html
index 2ab3dc62..47d7f8f7 100644
--- a/docs/reference/fit-method-cmdstan_summary.html
+++ b/docs/reference/fit-method-cmdstan_summary.html
@@ -149,7 +149,7 @@ Examples
# \dontrun{
fit <- cmdstanr_example("logistic")
fit$cmdstan_diagnose()
-#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-1-1f791d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-2-1f791d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-3-1f791d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-4-1f791d.csv
+#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-1-2718e9.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-2-2718e9.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-3-2718e9.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-4-2718e9.csv
#>
#> Checking sampler transitions treedepth.
#> Treedepth satisfactory for all transitions.
@@ -169,123 +169,123 @@ Examples
#> Inference for Stan model: logistic_model
#> 4 chains: each with iter=(1000,1000,1000,1000); warmup=(0,0,0,0); thin=(1,1,1,1); 4000 iterations saved.
#>
-#> Warmup took (0.023, 0.023, 0.023, 0.023) seconds, 0.092 seconds total
-#> Sampling took (0.076, 0.070, 0.072, 0.073) seconds, 0.29 seconds total
+#> Warmup took (0.022, 0.023, 0.023, 0.023) seconds, 0.091 seconds total
+#> Sampling took (0.071, 0.070, 0.070, 0.070) seconds, 0.28 seconds total
#>
#> Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
#>
-#> lp__ -6.6e+01 3.0e-02 1.4 -69 -6.6e+01 -6.4e+01 2277 7826 1.0
-#> accept_stat__ 0.89 1.8e-03 0.12 0.63 0.94 1.0 4996 17168 1.0e+00
-#> stepsize__ 0.80 4.6e-02 0.066 0.73 0.85 0.88 2.0 6.9 2.6e+13
-#> treedepth__ 2.3 8.8e-02 0.50 2.0 2.0 3.0 32 109 1.0e+00
-#> n_leapfrog__ 4.8 2.8e-01 2.0 3.0 3.0 7.0 53 182 1.0e+00
+#> lp__ -6.6e+01 3.1e-02 1.4 -69 -6.6e+01 -6.4e+01 2120 7545 1.0
+#> accept_stat__ 0.91 1.4e-03 0.10 0.69 0.95 1.0 5437 19350 1.0e+00
+#> stepsize__ 0.74 2.1e-02 0.030 0.70 0.75 0.78 2.0 7.1 1.1e+13
+#> treedepth__ 2.4 8.8e-03 0.53 2.0 2.0 3.0 3601 12815 1.0e+00
+#> n_leapfrog__ 5.3 3.3e-02 2.0 3.0 7.0 7.0 3773 13428 1.0e+00
#> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 nan nan nan
-#> energy__ 68 4.5e-02 2.0 65 68 72 1941 6671 1.0e+00
+#> energy__ 68 4.9e-02 2.0 65 68 72 1750 6227 1.0e+00
#>
-#> alpha 3.8e-01 3.5e-03 0.22 0.014 3.8e-01 7.4e-01 4054 13931 1.0
-#> beta[1] -6.7e-01 3.7e-03 0.25 -1.1 -6.7e-01 -2.8e-01 4566 15692 1.0
-#> beta[2] -2.8e-01 3.9e-03 0.23 -0.66 -2.8e-01 9.5e-02 3521 12100 1.0
-#> beta[3] 6.8e-01 4.0e-03 0.26 0.25 6.8e-01 1.1e+00 4334 14894 1.0
-#> log_lik[1] -5.2e-01 1.6e-03 0.10 -0.69 -5.1e-01 -3.6e-01 4009 13775 1.0
-#> log_lik[2] -4.0e-01 2.2e-03 0.15 -0.69 -3.8e-01 -1.9e-01 4671 16052 1.0
-#> log_lik[3] -5.0e-01 3.7e-03 0.22 -0.91 -4.7e-01 -2.1e-01 3570 12267 1.0
-#> log_lik[4] -4.5e-01 2.4e-03 0.15 -0.72 -4.3e-01 -2.3e-01 3825 13143 1.0
-#> log_lik[5] -1.2e+00 4.1e-03 0.28 -1.7 -1.2e+00 -7.6e-01 4632 15916 1.0
-#> log_lik[6] -5.9e-01 2.8e-03 0.19 -0.93 -5.7e-01 -3.2e-01 4602 15813 1.0
-#> log_lik[7] -6.4e-01 1.9e-03 0.13 -0.86 -6.3e-01 -4.5e-01 4425 15206 1.0
-#> log_lik[8] -2.7e-01 2.3e-03 0.14 -0.54 -2.5e-01 -1.0e-01 3551 12204 1.0
-#> log_lik[9] -6.9e-01 2.6e-03 0.17 -0.99 -6.8e-01 -4.4e-01 4396 15105 1.0
-#> log_lik[10] -7.5e-01 4.0e-03 0.24 -1.2 -7.2e-01 -4.1e-01 3524 12111 1.0
-#> log_lik[11] -2.8e-01 1.9e-03 0.12 -0.51 -2.6e-01 -1.2e-01 4067 13978 1.00
-#> log_lik[12] -5.0e-01 3.5e-03 0.24 -0.94 -4.6e-01 -1.9e-01 4672 16056 1.00
-#> log_lik[13] -6.6e-01 3.0e-03 0.21 -1.0 -6.4e-01 -3.6e-01 4681 16086 1.0
-#> log_lik[14] -3.6e-01 2.9e-03 0.17 -0.69 -3.3e-01 -1.4e-01 3667 12602 1.0
-#> log_lik[15] -2.8e-01 1.6e-03 0.11 -0.47 -2.6e-01 -1.4e-01 4253 14617 1.0
-#> log_lik[16] -2.7e-01 1.5e-03 0.087 -0.43 -2.6e-01 -1.5e-01 3572 12276 1.0
-#> log_lik[17] -1.6e+00 4.6e-03 0.29 -2.1 -1.6e+00 -1.2e+00 3940 13539 1.00
-#> log_lik[18] -4.8e-01 1.7e-03 0.11 -0.68 -4.7e-01 -3.1e-01 4208 14461 1.0
-#> log_lik[19] -2.3e-01 1.3e-03 0.075 -0.37 -2.2e-01 -1.2e-01 3488 11985 1.00
-#> log_lik[20] -1.1e-01 1.3e-03 0.078 -0.26 -9.3e-02 -2.8e-02 3400 11685 1.0
-#> log_lik[21] -2.1e-01 1.4e-03 0.086 -0.37 -2.0e-01 -9.3e-02 3747 12875 1.0
-#> log_lik[22] -5.7e-01 2.3e-03 0.15 -0.84 -5.5e-01 -3.5e-01 4159 14294 1.0
-#> log_lik[23] -3.3e-01 2.3e-03 0.14 -0.60 -3.0e-01 -1.4e-01 3876 13319 1.0
-#> log_lik[24] -1.3e-01 1.1e-03 0.066 -0.26 -1.2e-01 -5.1e-02 3311 11379 1.0
-#> log_lik[25] -4.5e-01 1.9e-03 0.12 -0.67 -4.4e-01 -2.7e-01 4263 14650 1.0
-#> log_lik[26] -1.5e+00 4.9e-03 0.34 -2.1 -1.5e+00 -1.0e+00 4863 16712 1.00
-#> log_lik[27] -3.0e-01 1.9e-03 0.12 -0.53 -2.9e-01 -1.4e-01 3942 13545 1.0
-#> log_lik[28] -4.4e-01 1.3e-03 0.085 -0.59 -4.4e-01 -3.1e-01 4011 13785 1.0
-#> log_lik[29] -7.2e-01 3.4e-03 0.23 -1.1 -6.9e-01 -3.9e-01 4401 15125 1.0
-#> log_lik[30] -6.9e-01 2.9e-03 0.20 -1.0 -6.7e-01 -4.0e-01 4483 15406 1.0
-#> log_lik[31] -4.9e-01 2.5e-03 0.16 -0.78 -4.7e-01 -2.6e-01 4140 14226 1.0
-#> log_lik[32] -4.2e-01 1.6e-03 0.11 -0.61 -4.1e-01 -2.7e-01 4397 15110 1.0
-#> log_lik[33] -4.1e-01 1.9e-03 0.13 -0.64 -4.0e-01 -2.2e-01 4495 15446 1.0
-#> log_lik[34] -6.2e-02 8.6e-04 0.050 -0.16 -4.8e-02 -1.2e-02 3377 11605 1.0
-#> log_lik[35] -5.8e-01 2.5e-03 0.18 -0.90 -5.6e-01 -3.2e-01 5084 17471 1.00
-#> log_lik[36] -3.2e-01 2.0e-03 0.13 -0.56 -3.0e-01 -1.4e-01 4311 14814 1.0
-#> log_lik[37] -7.1e-01 3.7e-03 0.23 -1.1 -6.8e-01 -3.8e-01 3857 13253 1.0
-#> log_lik[38] -3.1e-01 2.3e-03 0.15 -0.58 -2.8e-01 -1.2e-01 3910 13435 1.0
-#> log_lik[39] -1.8e-01 1.8e-03 0.11 -0.39 -1.5e-01 -5.3e-02 3559 12229 1.0
-#> log_lik[40] -6.8e-01 2.0e-03 0.13 -0.92 -6.7e-01 -4.8e-01 4390 15085 1.0
-#> log_lik[41] -1.1e+00 4.2e-03 0.26 -1.6 -1.1e+00 -7.5e-01 3790 13024 1.0
-#> log_lik[42] -9.3e-01 3.1e-03 0.20 -1.3 -9.1e-01 -6.3e-01 4446 15278 1.0
-#> log_lik[43] -4.0e-01 3.5e-03 0.25 -0.89 -3.5e-01 -1.0e-01 5239 18003 1.00
-#> log_lik[44] -1.2e+00 3.0e-03 0.19 -1.5 -1.2e+00 -8.9e-01 3904 13415 1.0
-#> log_lik[45] -3.6e-01 1.8e-03 0.12 -0.57 -3.4e-01 -1.9e-01 4092 14061 1.0
-#> log_lik[46] -5.8e-01 2.0e-03 0.13 -0.82 -5.7e-01 -3.8e-01 4539 15598 1.0
-#> log_lik[47] -3.0e-01 2.0e-03 0.13 -0.55 -2.8e-01 -1.3e-01 4158 14288 1.0
-#> log_lik[48] -3.2e-01 1.3e-03 0.083 -0.47 -3.1e-01 -2.0e-01 4285 14726 1.00
-#> log_lik[49] -3.2e-01 1.3e-03 0.080 -0.46 -3.1e-01 -2.0e-01 3835 13178 1.0
-#> log_lik[50] -1.3e+00 4.6e-03 0.33 -1.9 -1.3e+00 -8.0e-01 5110 17559 1.00
-#> log_lik[51] -2.9e-01 1.4e-03 0.094 -0.45 -2.7e-01 -1.5e-01 4316 14833 1.00
-#> log_lik[52] -8.3e-01 2.3e-03 0.14 -1.1 -8.3e-01 -6.1e-01 4123 14167 1.0
-#> log_lik[53] -4.0e-01 2.0e-03 0.13 -0.64 -3.8e-01 -2.2e-01 3978 13669 1.0
-#> log_lik[54] -3.7e-01 2.2e-03 0.14 -0.63 -3.5e-01 -1.8e-01 4204 14447 1.0
-#> log_lik[55] -3.9e-01 2.0e-03 0.14 -0.64 -3.7e-01 -1.9e-01 4560 15671 1.0
-#> log_lik[56] -3.1e-01 2.9e-03 0.19 -0.68 -2.7e-01 -8.9e-02 4172 14335 1.0
-#> log_lik[57] -6.6e-01 1.9e-03 0.12 -0.87 -6.5e-01 -4.8e-01 4000 13747 1.0
-#> log_lik[58] -9.6e-01 5.3e-03 0.35 -1.6 -9.1e-01 -4.6e-01 4421 15193 1.0
-#> log_lik[59] -1.4e+00 5.3e-03 0.35 -2.0 -1.3e+00 -8.4e-01 4356 14970 1.0
-#> log_lik[60] -9.8e-01 2.5e-03 0.16 -1.3 -9.7e-01 -7.3e-01 4051 13921 1.0
-#> log_lik[61] -5.4e-01 1.5e-03 0.10 -0.71 -5.3e-01 -3.9e-01 4197 14422 1.0
-#> log_lik[62] -8.8e-01 4.6e-03 0.31 -1.4 -8.4e-01 -4.4e-01 4561 15674 1.00
-#> log_lik[63] -1.1e-01 1.3e-03 0.072 -0.26 -9.8e-02 -3.2e-02 3248 11162 1.0
-#> log_lik[64] -9.1e-01 3.9e-03 0.26 -1.4 -8.9e-01 -5.3e-01 4383 15063 1.0
-#> log_lik[65] -2.0e+00 9.5e-03 0.59 -3.0 -2.0e+00 -1.1e+00 3828 13154 1.00
-#> log_lik[66] -5.1e-01 2.0e-03 0.13 -0.75 -5.0e-01 -3.1e-01 4591 15777 1.0
-#> log_lik[67] -2.7e-01 1.3e-03 0.082 -0.42 -2.7e-01 -1.6e-01 3737 12841 1.00
-#> log_lik[68] -1.1e+00 3.9e-03 0.24 -1.5 -1.1e+00 -7.0e-01 3952 13582 1.0
-#> log_lik[69] -4.4e-01 1.3e-03 0.085 -0.59 -4.3e-01 -3.1e-01 3944 13552 1.0
-#> log_lik[70] -6.3e-01 3.4e-03 0.24 -1.1 -6.0e-01 -3.0e-01 5018 17243 1.0
-#> log_lik[71] -6.1e-01 2.8e-03 0.21 -0.98 -5.9e-01 -3.2e-01 5227 17963 1.00
-#> log_lik[72] -4.6e-01 2.6e-03 0.17 -0.78 -4.4e-01 -2.3e-01 4552 15644 1.0
-#> log_lik[73] -1.5e+00 5.9e-03 0.37 -2.2 -1.5e+00 -9.4e-01 3999 13742 1.0
-#> log_lik[74] -9.4e-01 3.0e-03 0.19 -1.3 -9.3e-01 -6.5e-01 4092 14063 1.0
-#> log_lik[75] -1.1e+00 5.6e-03 0.38 -1.8 -1.1e+00 -5.9e-01 4639 15941 1.00
-#> log_lik[76] -3.7e-01 2.3e-03 0.14 -0.63 -3.5e-01 -1.8e-01 3728 12812 1.0
-#> log_lik[77] -8.8e-01 2.2e-03 0.14 -1.1 -8.7e-01 -6.6e-01 4154 14274 1.0
-#> log_lik[78] -4.9e-01 2.4e-03 0.17 -0.80 -4.7e-01 -2.5e-01 4939 16971 1.00
-#> log_lik[79] -7.7e-01 3.2e-03 0.19 -1.1 -7.5e-01 -4.9e-01 3734 12831 1.0
-#> log_lik[80] -5.5e-01 2.8e-03 0.20 -0.91 -5.2e-01 -2.7e-01 5052 17361 1.0
-#> log_lik[81] -1.6e-01 1.6e-03 0.100 -0.35 -1.4e-01 -4.6e-02 4042 13890 1.0
-#> log_lik[82] -2.2e-01 2.1e-03 0.14 -0.50 -1.9e-01 -6.1e-02 4473 15371 1.0
-#> log_lik[83] -3.4e-01 1.3e-03 0.081 -0.49 -3.4e-01 -2.2e-01 3946 13559 1.0
-#> log_lik[84] -2.8e-01 1.4e-03 0.091 -0.44 -2.6e-01 -1.5e-01 4018 13807 1.0
-#> log_lik[85] -1.3e-01 1.3e-03 0.074 -0.27 -1.1e-01 -4.2e-02 3489 11990 1.0
-#> log_lik[86] -1.1e+00 4.7e-03 0.32 -1.7 -1.1e+00 -6.4e-01 4742 16295 1.0
-#> log_lik[87] -8.3e-01 2.0e-03 0.13 -1.0 -8.2e-01 -6.2e-01 4289 14739 1.0
-#> log_lik[88] -7.8e-01 3.5e-03 0.24 -1.2 -7.5e-01 -4.3e-01 4606 15828 1.00
-#> log_lik[89] -1.3e+00 4.9e-03 0.32 -1.9 -1.3e+00 -7.9e-01 4346 14936 1.0
-#> log_lik[90] -2.6e-01 2.1e-03 0.13 -0.51 -2.3e-01 -9.0e-02 4192 14407 1.00
-#> log_lik[91] -3.9e-01 1.9e-03 0.13 -0.62 -3.7e-01 -2.0e-01 4638 15940 1.0
-#> log_lik[92] -1.5e+00 5.2e-03 0.34 -2.1 -1.5e+00 -9.9e-01 4259 14636 1.0
-#> log_lik[93] -7.3e-01 3.6e-03 0.22 -1.1 -7.1e-01 -4.2e-01 3742 12858 1.0
-#> log_lik[94] -3.2e-01 1.4e-03 0.088 -0.48 -3.1e-01 -1.9e-01 3956 13596 1.0
-#> log_lik[95] -3.9e-01 1.8e-03 0.11 -0.59 -3.8e-01 -2.3e-01 3846 13217 1.0
-#> log_lik[96] -1.6e+00 4.5e-03 0.28 -2.1 -1.6e+00 -1.1e+00 3868 13293 1.0
-#> log_lik[97] -4.3e-01 1.5e-03 0.10 -0.61 -4.2e-01 -2.7e-01 4724 16235 1.0
-#> log_lik[98] -1.1e+00 5.5e-03 0.37 -1.7 -1.0e+00 -5.2e-01 4560 15668 1.0
-#> log_lik[99] -7.0e-01 2.2e-03 0.15 -0.95 -6.8e-01 -4.7e-01 4389 15084 1.0
-#> log_lik[100] -3.9e-01 1.5e-03 0.097 -0.56 -3.8e-01 -2.5e-01 4436 15244 1.00
+#> alpha 3.8e-01 3.5e-03 0.22 0.026 3.8e-01 7.5e-01 3978 14156 1.00
+#> beta[1] -6.7e-01 4.1e-03 0.26 -1.1 -6.6e-01 -2.6e-01 3779 13447 1.0
+#> beta[2] -2.7e-01 3.5e-03 0.23 -0.64 -2.7e-01 1.0e-01 4061 14450 1.0
+#> beta[3] 6.7e-01 4.3e-03 0.27 0.24 6.7e-01 1.1e+00 3879 13804 1.00
+#> log_lik[1] -5.1e-01 1.5e-03 0.097 -0.68 -5.1e-01 -3.7e-01 3951 14060 1.00
+#> log_lik[2] -4.1e-01 2.3e-03 0.15 -0.69 -3.9e-01 -1.9e-01 4167 14830 1.00
+#> log_lik[3] -4.9e-01 3.3e-03 0.21 -0.88 -4.6e-01 -2.1e-01 4010 14272 1.00
+#> log_lik[4] -4.5e-01 2.5e-03 0.15 -0.72 -4.4e-01 -2.4e-01 3751 13350 1.0
+#> log_lik[5] -1.2e+00 4.4e-03 0.28 -1.7 -1.2e+00 -7.6e-01 4068 14476 1.0
+#> log_lik[6] -5.9e-01 3.0e-03 0.19 -0.96 -5.7e-01 -3.2e-01 4275 15213 1.0
+#> log_lik[7] -6.4e-01 2.0e-03 0.13 -0.86 -6.4e-01 -4.5e-01 4183 14885 1.0
+#> log_lik[8] -2.8e-01 2.3e-03 0.14 -0.55 -2.5e-01 -1.1e-01 3659 13021 1.0
+#> log_lik[9] -6.9e-01 2.7e-03 0.17 -1.0 -6.7e-01 -4.4e-01 4128 14690 1.0
+#> log_lik[10] -7.3e-01 3.6e-03 0.22 -1.1 -7.1e-01 -4.1e-01 3894 13857 1.00
+#> log_lik[11] -2.8e-01 2.1e-03 0.13 -0.54 -2.6e-01 -1.1e-01 3822 13601 1.00
+#> log_lik[12] -5.0e-01 3.8e-03 0.24 -0.95 -4.6e-01 -1.9e-01 3952 14065 1.00
+#> log_lik[13] -6.6e-01 3.3e-03 0.21 -1.0 -6.4e-01 -3.5e-01 4181 14878 1.0
+#> log_lik[14] -3.6e-01 2.6e-03 0.17 -0.67 -3.2e-01 -1.4e-01 4021 14308 1.00
+#> log_lik[15] -2.8e-01 1.7e-03 0.10 -0.47 -2.6e-01 -1.3e-01 3858 13730 1.00
+#> log_lik[16] -2.8e-01 1.5e-03 0.091 -0.44 -2.7e-01 -1.5e-01 3706 13189 1.00
+#> log_lik[17] -1.6e+00 5.0e-03 0.30 -2.1 -1.6e+00 -1.1e+00 3612 12854 1.0
+#> log_lik[18] -4.8e-01 1.8e-03 0.11 -0.68 -4.7e-01 -3.1e-01 4081 14523 1.0
+#> log_lik[19] -2.3e-01 1.3e-03 0.079 -0.37 -2.2e-01 -1.2e-01 3751 13347 1.0
+#> log_lik[20] -1.1e-01 1.3e-03 0.078 -0.26 -9.4e-02 -2.8e-02 3798 13516 1.00
+#> log_lik[21] -2.1e-01 1.5e-03 0.091 -0.39 -2.0e-01 -9.3e-02 3626 12906 1.00
+#> log_lik[22] -5.7e-01 2.4e-03 0.15 -0.86 -5.5e-01 -3.5e-01 4061 14452 1.0
+#> log_lik[23] -3.3e-01 2.3e-03 0.15 -0.61 -3.1e-01 -1.4e-01 3872 13780 1.0
+#> log_lik[24] -1.4e-01 1.1e-03 0.069 -0.27 -1.2e-01 -5.1e-02 3743 13321 1.00
+#> log_lik[25] -4.6e-01 1.9e-03 0.12 -0.68 -4.5e-01 -2.8e-01 3975 14145 1.0
+#> log_lik[26] -1.5e+00 5.8e-03 0.35 -2.1 -1.5e+00 -1.0e+00 3699 13164 1.0
+#> log_lik[27] -3.1e-01 2.0e-03 0.13 -0.55 -2.9e-01 -1.4e-01 3831 13633 1.00
+#> log_lik[28] -4.4e-01 1.4e-03 0.086 -0.59 -4.4e-01 -3.1e-01 3970 14130 1.0
+#> log_lik[29] -7.3e-01 3.4e-03 0.22 -1.1 -7.1e-01 -4.0e-01 4258 15153 1.0
+#> log_lik[30] -6.9e-01 3.1e-03 0.20 -1.1 -6.8e-01 -4.0e-01 4121 14666 1.00
+#> log_lik[31] -4.9e-01 2.6e-03 0.17 -0.81 -4.7e-01 -2.5e-01 4046 14398 1.0
+#> log_lik[32] -4.2e-01 1.7e-03 0.11 -0.62 -4.1e-01 -2.6e-01 3986 14185 1.0
+#> log_lik[33] -4.1e-01 2.0e-03 0.13 -0.65 -4.0e-01 -2.2e-01 4122 14669 1.0
+#> log_lik[34] -6.5e-02 9.1e-04 0.054 -0.17 -5.0e-02 -1.3e-02 3544 12612 1.0
+#> log_lik[35] -5.9e-01 2.8e-03 0.18 -0.92 -5.7e-01 -3.2e-01 4411 15696 1.0
+#> log_lik[36] -3.3e-01 2.2e-03 0.14 -0.58 -3.0e-01 -1.4e-01 4001 14239 1.0
+#> log_lik[37] -6.9e-01 3.4e-03 0.22 -1.1 -6.7e-01 -3.7e-01 4045 14394 1.0
+#> log_lik[38] -3.2e-01 2.4e-03 0.15 -0.60 -2.9e-01 -1.2e-01 3854 13717 1.0
+#> log_lik[39] -1.8e-01 1.7e-03 0.11 -0.38 -1.5e-01 -5.2e-02 3915 13934 1.00
+#> log_lik[40] -6.8e-01 2.1e-03 0.13 -0.92 -6.7e-01 -4.8e-01 4145 14752 1.0
+#> log_lik[41] -1.1e+00 4.2e-03 0.26 -1.6 -1.1e+00 -7.3e-01 3929 13983 1.0
+#> log_lik[42] -9.3e-01 3.2e-03 0.20 -1.3 -9.1e-01 -6.3e-01 3967 14116 1.0
+#> log_lik[43] -4.1e-01 4.0e-03 0.26 -0.91 -3.4e-01 -1.0e-01 4191 14913 1.0
+#> log_lik[44] -1.2e+00 3.1e-03 0.19 -1.5 -1.2e+00 -9.0e-01 3709 13201 1.00
+#> log_lik[45] -3.6e-01 1.8e-03 0.11 -0.56 -3.4e-01 -1.9e-01 4009 14267 1.00
+#> log_lik[46] -5.9e-01 2.0e-03 0.13 -0.82 -5.8e-01 -3.8e-01 4248 15119 1.00
+#> log_lik[47] -3.1e-01 2.2e-03 0.13 -0.55 -2.9e-01 -1.3e-01 3696 13153 1.0
+#> log_lik[48] -3.2e-01 1.4e-03 0.085 -0.47 -3.2e-01 -2.0e-01 3847 13691 1.0
+#> log_lik[49] -3.2e-01 1.3e-03 0.082 -0.46 -3.1e-01 -2.0e-01 3716 13223 1.00
+#> log_lik[50] -1.3e+00 5.4e-03 0.33 -1.9 -1.3e+00 -8.1e-01 3859 13734 1.0
+#> log_lik[51] -2.9e-01 1.6e-03 0.096 -0.46 -2.8e-01 -1.5e-01 3714 13218 1.0
+#> log_lik[52] -8.4e-01 2.2e-03 0.14 -1.1 -8.3e-01 -6.2e-01 3985 14182 1.00
+#> log_lik[53] -4.0e-01 2.1e-03 0.13 -0.66 -3.9e-01 -2.1e-01 3970 14127 1.00
+#> log_lik[54] -3.7e-01 2.2e-03 0.14 -0.62 -3.5e-01 -1.8e-01 4008 14263 1.00
+#> log_lik[55] -3.9e-01 2.2e-03 0.14 -0.65 -3.7e-01 -1.9e-01 4107 14614 1.00
+#> log_lik[56] -3.2e-01 3.2e-03 0.20 -0.70 -2.8e-01 -9.3e-02 3745 13326 1.0
+#> log_lik[57] -6.5e-01 1.8e-03 0.12 -0.85 -6.5e-01 -4.8e-01 3918 13943 1.0
+#> log_lik[58] -9.4e-01 5.3e-03 0.35 -1.6 -9.0e-01 -4.5e-01 4317 15364 1.0
+#> log_lik[59] -1.4e+00 5.7e-03 0.35 -2.0 -1.3e+00 -8.4e-01 3801 13527 1.0
+#> log_lik[60] -9.8e-01 2.5e-03 0.16 -1.3 -9.7e-01 -7.4e-01 3902 13886 1.00
+#> log_lik[61] -5.4e-01 1.5e-03 0.097 -0.70 -5.3e-01 -3.9e-01 4078 14513 1.00
+#> log_lik[62] -8.9e-01 5.0e-03 0.31 -1.5 -8.5e-01 -4.4e-01 3852 13708 1.00
+#> log_lik[63] -1.2e-01 1.3e-03 0.077 -0.27 -9.9e-02 -3.2e-02 3330 11849 1.00
+#> log_lik[64] -9.0e-01 3.8e-03 0.25 -1.4 -8.8e-01 -5.3e-01 4445 15817 1.00
+#> log_lik[65] -2.0e+00 9.8e-03 0.61 -3.1 -2.0e+00 -1.1e+00 3783 13462 1.00
+#> log_lik[66] -5.1e-01 2.1e-03 0.13 -0.74 -4.9e-01 -3.1e-01 4154 14782 1.0
+#> log_lik[67] -2.8e-01 1.4e-03 0.085 -0.43 -2.7e-01 -1.6e-01 3827 13618 1.0
+#> log_lik[68] -1.1e+00 3.8e-03 0.24 -1.5 -1.0e+00 -6.8e-01 4102 14597 1.00
+#> log_lik[69] -4.3e-01 1.4e-03 0.085 -0.58 -4.3e-01 -3.0e-01 3842 13673 1.0
+#> log_lik[70] -6.4e-01 3.6e-03 0.24 -1.1 -6.1e-01 -3.1e-01 4371 15555 1.00
+#> log_lik[71] -6.1e-01 3.2e-03 0.21 -0.99 -5.9e-01 -3.1e-01 4401 15663 1.0
+#> log_lik[72] -4.6e-01 2.7e-03 0.17 -0.79 -4.5e-01 -2.2e-01 4214 14996 1.0
+#> log_lik[73] -1.5e+00 6.1e-03 0.37 -2.1 -1.4e+00 -9.2e-01 3671 13065 1.0
+#> log_lik[74] -9.5e-01 3.0e-03 0.19 -1.3 -9.4e-01 -6.7e-01 3944 14035 1.00
+#> log_lik[75] -1.2e+00 6.4e-03 0.39 -1.8 -1.1e+00 -5.9e-01 3678 13088 1.00
+#> log_lik[76] -3.7e-01 2.1e-03 0.13 -0.62 -3.5e-01 -1.8e-01 3976 14151 1.00
+#> log_lik[77] -8.8e-01 2.2e-03 0.14 -1.1 -8.8e-01 -6.6e-01 4034 14357 1.0
+#> log_lik[78] -4.8e-01 2.7e-03 0.17 -0.79 -4.6e-01 -2.4e-01 4097 14581 1.00
+#> log_lik[79] -7.6e-01 2.9e-03 0.18 -1.1 -7.4e-01 -4.8e-01 3889 13840 1.0
+#> log_lik[80] -5.5e-01 3.0e-03 0.20 -0.91 -5.2e-01 -2.7e-01 4407 15683 1.00
+#> log_lik[81] -1.7e-01 1.7e-03 0.10 -0.37 -1.4e-01 -4.8e-02 3780 13450 1.0
+#> log_lik[82] -2.3e-01 2.3e-03 0.14 -0.51 -2.0e-01 -6.3e-02 3943 14031 1.00
+#> log_lik[83] -3.4e-01 1.3e-03 0.081 -0.48 -3.4e-01 -2.2e-01 3766 13404 1.00
+#> log_lik[84] -2.7e-01 1.5e-03 0.090 -0.44 -2.6e-01 -1.4e-01 3767 13405 1.00
+#> log_lik[85] -1.3e-01 1.2e-03 0.075 -0.28 -1.1e-01 -4.1e-02 3780 13453 1.00
+#> log_lik[86] -1.1e+00 5.0e-03 0.32 -1.7 -1.1e+00 -6.4e-01 4173 14850 1.00
+#> log_lik[87] -8.3e-01 2.0e-03 0.13 -1.1 -8.2e-01 -6.2e-01 4183 14887 1.00
+#> log_lik[88] -7.8e-01 3.8e-03 0.24 -1.2 -7.5e-01 -4.2e-01 4157 14794 1.0
+#> log_lik[89] -1.3e+00 5.1e-03 0.32 -1.9 -1.2e+00 -7.9e-01 3879 13805 1.00
+#> log_lik[90] -2.6e-01 2.2e-03 0.14 -0.52 -2.4e-01 -9.4e-02 4008 14262 1.0
+#> log_lik[91] -3.9e-01 2.1e-03 0.13 -0.63 -3.8e-01 -2.1e-01 4144 14746 1.0
+#> log_lik[92] -1.5e+00 5.6e-03 0.34 -2.1 -1.5e+00 -9.7e-01 3829 13625 1.00
+#> log_lik[93] -7.5e-01 3.4e-03 0.21 -1.1 -7.2e-01 -4.4e-01 3888 13835 1.00
+#> log_lik[94] -3.2e-01 1.4e-03 0.087 -0.47 -3.1e-01 -1.9e-01 3803 13533 1.00
+#> log_lik[95] -3.9e-01 1.9e-03 0.12 -0.60 -3.8e-01 -2.2e-01 3889 13841 1.0
+#> log_lik[96] -1.6e+00 4.8e-03 0.29 -2.1 -1.6e+00 -1.1e+00 3601 12815 1.00
+#> log_lik[97] -4.3e-01 1.6e-03 0.10 -0.61 -4.2e-01 -2.7e-01 4067 14472 1.0
+#> log_lik[98] -1.0e+00 5.6e-03 0.37 -1.7 -1.0e+00 -5.2e-01 4342 15452 1.0
+#> log_lik[99] -7.0e-01 2.2e-03 0.15 -0.96 -6.9e-01 -4.7e-01 4351 15484 1.00
+#> log_lik[100] -3.9e-01 1.5e-03 0.097 -0.55 -3.8e-01 -2.4e-01 4019 14301 1.00
#>
#> Samples were drawn using hmc with nuts.
#> For each parameter, N_Eff is a crude measure of effective sample size,
diff --git a/docs/reference/fit-method-diagnostic_summary.html b/docs/reference/fit-method-diagnostic_summary.html
index 97dce2c2..53c45c1c 100644
--- a/docs/reference/fit-method-diagnostic_summary.html
+++ b/docs/reference/fit-method-diagnostic_summary.html
@@ -161,34 +161,29 @@ See also
Examples
# \dontrun{
fit <- cmdstanr_example("schools")
-#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
-#> Chain 2 Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model-11d99572dc9ce.stan', line 14, column 2 to column 41)
-#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
-#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
-#> Chain 2
-#> Warning: 103 of 4000 (3.0%) transitions ended with a divergence.
+#> Warning: 91 of 4000 (2.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.
fit$diagnostic_summary()
-#> Warning: 103 of 4000 (3.0%) transitions ended with a divergence.
+#> Warning: 91 of 4000 (2.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.
#> $num_divergent
-#> [1] 2 40 38 23
+#> [1] 12 18 11 50
#>
#> $num_max_treedepth
#> [1] 0 0 0 0
#>
#> $ebfmi
-#> [1] 0.2865692 0.3867145 0.2763028 0.3720129
+#> [1] 0.3003970 0.2974182 0.3438194 0.4920025
#>
fit$diagnostic_summary(quiet = TRUE)
#> $num_divergent
-#> [1] 2 40 38 23
+#> [1] 12 18 11 50
#>
#> $num_max_treedepth
#> [1] 0 0 0 0
#>
#> $ebfmi
-#> [1] 0.2865692 0.3867145 0.2763028 0.3720129
+#> [1] 0.3003970 0.2974182 0.3438194 0.4920025
#>
# }
diff --git a/docs/reference/fit-method-draws-1.png b/docs/reference/fit-method-draws-1.png
index 27dbcc21..a6899fc4 100644
Binary files a/docs/reference/fit-method-draws-1.png and b/docs/reference/fit-method-draws-1.png differ
diff --git a/docs/reference/fit-method-draws-2.png b/docs/reference/fit-method-draws-2.png
index 78e9ff03..fb93238c 100644
Binary files a/docs/reference/fit-method-draws-2.png and b/docs/reference/fit-method-draws-2.png differ
diff --git a/docs/reference/fit-method-draws.html b/docs/reference/fit-method-draws.html
index 1a2ac8f7..b257cb5d 100644
--- a/docs/reference/fit-method-draws.html
+++ b/docs/reference/fit-method-draws.html
@@ -203,7 +203,7 @@ Examples
dim(draws)
#> [1] 1000 4 105
str(draws)
-#> 'draws_array' num [1:1000, 1:4, 1:105] -65.8 -67 -66.3 -66.2 -66.8 ...
+#> 'draws_array' num [1:1000, 1:4, 1:105] -64.2 -65.3 -65.3 -64.7 -64.8 ...
#> - attr(*, "dimnames")=List of 3
#> ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
#> ..$ chain : chr [1:4] "1" "2" "3" "4"
@@ -215,12 +215,12 @@ Examples
#> # A draws_matrix: 6 iterations, 1 chains, and 105 variables
#> variable
#> draw lp__ alpha beta[1] beta[2] beta[3] log_lik[1] log_lik[2] log_lik[3]
-#> 1 -66 0.24 -0.55 0.09 0.39 -0.52 -0.42 -0.32
-#> 2 -67 0.52 -0.85 -0.20 1.28 -0.46 -0.18 -0.26
-#> 3 -66 0.19 -0.80 -0.31 0.20 -0.54 -0.53 -0.62
-#> 4 -66 0.39 -0.57 -0.68 1.03 -0.59 -0.33 -0.81
-#> 5 -67 0.46 -0.49 -0.63 1.16 -0.57 -0.30 -0.72
-#> 6 -65 0.30 -0.44 -0.21 0.28 -0.54 -0.59 -0.56
+#> 1 -64 0.28 -0.73 -0.16 0.73 -0.53 -0.30 -0.39
+#> 2 -65 0.19 -0.42 -0.13 0.37 -0.59 -0.48 -0.53
+#> 3 -65 0.61 -0.93 -0.25 0.83 -0.40 -0.34 -0.30
+#> 4 -65 0.42 -0.81 -0.32 0.94 -0.50 -0.28 -0.41
+#> 5 -65 0.36 -0.41 -0.20 0.39 -0.52 -0.56 -0.52
+#> 6 -67 0.55 -0.24 -0.43 0.24 -0.49 -0.87 -0.75
#> # ... with 97 more variables
# or can specify 'format' argument to avoid manual conversion
@@ -230,12 +230,12 @@ Examples
#> # A draws_matrix: 6 iterations, 1 chains, and 105 variables
#> variable
#> draw lp__ alpha beta[1] beta[2] beta[3] log_lik[1] log_lik[2] log_lik[3]
-#> 1 -66 0.24 -0.55 0.09 0.39 -0.52 -0.42 -0.32
-#> 2 -67 0.52 -0.85 -0.20 1.28 -0.46 -0.18 -0.26
-#> 3 -66 0.19 -0.80 -0.31 0.20 -0.54 -0.53 -0.62
-#> 4 -66 0.39 -0.57 -0.68 1.03 -0.59 -0.33 -0.81
-#> 5 -67 0.46 -0.49 -0.63 1.16 -0.57 -0.30 -0.72
-#> 6 -65 0.30 -0.44 -0.21 0.28 -0.54 -0.59 -0.56
+#> 1 -64 0.28 -0.73 -0.16 0.73 -0.53 -0.30 -0.39
+#> 2 -65 0.19 -0.42 -0.13 0.37 -0.59 -0.48 -0.53
+#> 3 -65 0.61 -0.93 -0.25 0.83 -0.40 -0.34 -0.30
+#> 4 -65 0.42 -0.81 -0.32 0.94 -0.50 -0.28 -0.41
+#> 5 -65 0.36 -0.41 -0.20 0.39 -0.52 -0.56 -0.52
+#> 6 -67 0.55 -0.24 -0.43 0.24 -0.49 -0.87 -0.75
#> # ... with 97 more variables
# can select specific parameters
@@ -244,12 +244,12 @@ Examples
#> , , variable = alpha
#>
#> chain
-#> iteration 1 2 3 4
-#> 1 0.24 0.310 0.180 0.67
-#> 2 0.52 -0.014 0.351 0.23
-#> 3 0.19 0.366 0.682 0.48
-#> 4 0.39 0.645 0.098 0.49
-#> 5 0.46 0.177 0.677 0.65
+#> iteration 1 2 3 4
+#> 1 0.28 0.27 0.62 0.10
+#> 2 0.19 0.70 0.49 0.71
+#> 3 0.61 0.26 0.89 0.14
+#> 4 0.42 0.39 0.58 0.39
+#> 5 0.36 0.39 0.58 0.48
#>
#> # ... with 995 more iterations
fit$draws("beta") # selects entire vector beta
@@ -258,31 +258,31 @@ Examples
#>
#> chain
#> iteration 1 2 3 4
-#> 1 -0.55 -1.00 -0.41 -0.55
-#> 2 -0.85 -0.45 -0.62 -0.91
-#> 3 -0.80 -0.52 -0.86 -0.50
-#> 4 -0.57 -0.46 -0.50 -0.57
-#> 5 -0.49 -0.97 -1.00 -0.76
+#> 1 -0.73 -0.46 -0.82 -0.55
+#> 2 -0.42 -0.92 -1.18 -1.08
+#> 3 -0.93 -0.52 -0.63 -0.42
+#> 4 -0.81 -0.86 -0.30 -0.72
+#> 5 -0.41 -0.86 -0.30 -0.86
#>
#> , , variable = beta[2]
#>
#> chain
-#> iteration 1 2 3 4
-#> 1 0.09 -0.32 -0.132 -0.091
-#> 2 -0.20 -0.45 -0.283 -0.479
-#> 3 -0.31 -0.25 -0.335 -0.681
-#> 4 -0.68 -0.25 0.093 -0.717
-#> 5 -0.63 -0.23 -0.227 -0.328
+#> iteration 1 2 3 4
+#> 1 -0.16 -0.31 -0.88 -0.18
+#> 2 -0.13 -0.27 -0.69 -0.21
+#> 3 -0.25 -0.39 -0.38 -0.37
+#> 4 -0.32 -0.43 -0.52 -0.36
+#> 5 -0.20 -0.43 -0.52 -0.42
#>
#> , , variable = beta[3]
#>
#> chain
#> iteration 1 2 3 4
-#> 1 0.39 0.47 0.54 0.70
-#> 2 1.28 0.68 0.76 0.83
-#> 3 0.20 0.45 0.66 0.30
-#> 4 1.03 0.30 0.53 0.54
-#> 5 1.16 1.17 0.46 1.29
+#> 1 0.73 0.28 0.87 0.93
+#> 2 0.37 1.26 0.75 0.45
+#> 3 0.83 0.28 0.88 0.83
+#> 4 0.94 0.64 0.71 0.48
+#> 5 0.39 0.64 0.71 0.41
#>
#> # ... with 995 more iterations
fit$draws(c("alpha", "beta[2]"))
@@ -290,22 +290,22 @@ Examples
#> , , variable = alpha
#>
#> chain
-#> iteration 1 2 3 4
-#> 1 0.24 0.310 0.180 0.67
-#> 2 0.52 -0.014 0.351 0.23
-#> 3 0.19 0.366 0.682 0.48
-#> 4 0.39 0.645 0.098 0.49
-#> 5 0.46 0.177 0.677 0.65
+#> iteration 1 2 3 4
+#> 1 0.28 0.27 0.62 0.10
+#> 2 0.19 0.70 0.49 0.71
+#> 3 0.61 0.26 0.89 0.14
+#> 4 0.42 0.39 0.58 0.39
+#> 5 0.36 0.39 0.58 0.48
#>
#> , , variable = beta[2]
#>
#> chain
-#> iteration 1 2 3 4
-#> 1 0.09 -0.32 -0.132 -0.091
-#> 2 -0.20 -0.45 -0.283 -0.479
-#> 3 -0.31 -0.25 -0.335 -0.681
-#> 4 -0.68 -0.25 0.093 -0.717
-#> 5 -0.63 -0.23 -0.227 -0.328
+#> iteration 1 2 3 4
+#> 1 -0.16 -0.31 -0.88 -0.18
+#> 2 -0.13 -0.27 -0.69 -0.21
+#> 3 -0.25 -0.39 -0.38 -0.37
+#> 4 -0.32 -0.43 -0.52 -0.36
+#> 5 -0.20 -0.43 -0.52 -0.42
#>
#> # ... with 995 more iterations
@@ -323,21 +323,21 @@ Examples
#> # A draws_matrix: 6 iterations, 1 chains, and 3 variables
#> variable
#> draw beta[1] beta[2] beta[3]
-#> 1 -0.74 0.09 0.55
-#> 2 -0.76 0.39 0.68
-#> 3 -0.43 -0.15 1.06
-#> 4 -1.15 -0.35 0.54
-#> 5 -0.60 -0.26 0.34
-#> 6 -0.30 -0.24 1.03
+#> 1 -0.62 -0.485 0.63
+#> 2 -0.34 0.188 0.77
+#> 3 -0.63 -0.236 0.90
+#> 4 -0.65 -0.392 0.76
+#> 5 -0.57 0.028 0.44
+#> 6 -0.58 -0.299 0.69
head(fit$draws("beta", format = "df"))
#> # A draws_df: 6 iterations, 1 chains, and 3 variables
#> beta[1] beta[2] beta[3]
-#> 1 -0.74 0.09 0.55
-#> 2 -0.76 0.39 0.68
-#> 3 -0.43 -0.15 1.06
-#> 4 -1.15 -0.35 0.54
-#> 5 -0.60 -0.26 0.34
-#> 6 -0.30 -0.24 1.03
+#> 1 -0.62 -0.485 0.63
+#> 2 -0.34 0.188 0.77
+#> 3 -0.63 -0.236 0.90
+#> 4 -0.65 -0.392 0.76
+#> 5 -0.57 0.028 0.44
+#> 6 -0.58 -0.299 0.69
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
# }
diff --git a/docs/reference/fit-method-grad_log_prob.html b/docs/reference/fit-method-grad_log_prob.html
index 79de7ede..e6dae257 100644
--- a/docs/reference/fit-method-grad_log_prob.html
+++ b/docs/reference/fit-method-grad_log_prob.html
@@ -113,19 +113,26 @@ Calculate the log-probability and the gradient w.r.t. each input for a
- grad_log_prob(unconstrained_variables, jacobian_adjustment = TRUE)
+ grad_log_prob(
+ unconstrained_variables,
+ jacobian = TRUE,
+ jacobian_adjustment = NULL
+)
Arguments
- unconstrained_variables
-(numeric) A vector of unconstrained parameters
-to be passed to grad_log_prob.
(numeric) A vector of unconstrained parameters.
- jacobian
+(logical) Whether to include the log-density adjustments from
+un/constraining variables.
- jacobian_adjustment
-(logical) Whether to include the log-density
-adjustments from un/constraining variables.
Deprecated. Please use jacobian instead.
diff --git a/docs/reference/fit-method-gradients.html b/docs/reference/fit-method-gradients.html
index 828d0688..c682dd04 100644
--- a/docs/reference/fit-method-gradients.html
+++ b/docs/reference/fit-method-gradients.html
@@ -131,11 +131,11 @@ Examples
# retrieve the gradients
test$gradients()
-#> param_idx value model finite_diff error
-#> 1 0 1.376210 -19.10810 -19.10810 5.51201e-09
-#> 2 1 -0.796954 -2.04336 -2.04336 5.51900e-09
-#> 3 2 0.520069 -11.77830 -11.77830 -6.60224e-10
-#> 4 3 0.122833 7.41794 7.41794 4.62926e-08
+#> param_idx value model finite_diff error
+#> 1 0 1.083910 -6.21416 -6.21416 3.58858e-08
+#> 2 1 1.800640 -33.18970 -33.18970 4.38607e-08
+#> 3 2 0.683175 -19.70780 -19.70780 1.73640e-08
+#> 4 3 1.857330 -9.17992 -9.17992 1.12563e-08
# }
diff --git a/docs/reference/fit-method-hessian.html b/docs/reference/fit-method-hessian.html
index b08bb4ea..12bcc5e8 100644
--- a/docs/reference/fit-method-hessian.html
+++ b/docs/reference/fit-method-hessian.html
@@ -113,19 +113,22 @@ Calculate the log-probability , the gradient w.r.t. each input, and the hess
- hessian(unconstrained_variables, jacobian_adjustment = TRUE)
+ hessian(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = TRUE)
Arguments
- unconstrained_variables
-(numeric) A vector of unconstrained parameters
-to be passed to hessian.
(numeric) A vector of unconstrained parameters.
- jacobian
+(logical) Whether to include the log-density adjustments from
+un/constraining variables.
- jacobian_adjustment
-(logical) Whether to include the log-density
-adjustments from un/constraining variables.
Deprecated. Please use jacobian instead.
diff --git a/docs/reference/fit-method-init.html b/docs/reference/fit-method-init.html
index e6001ccc..955ed4b1 100644
--- a/docs/reference/fit-method-init.html
+++ b/docs/reference/fit-method-init.html
@@ -140,11 +140,11 @@ Examples
str(fit$init())
#> List of 2
#> $ :List of 2
-#> ..$ alpha: num 0.865
-#> ..$ beta : num [1:3] -0.0306 -0.1349 1.1272
+#> ..$ alpha: num -0.538
+#> ..$ beta : num [1:3] -0.0289 1.8267 1.6042
#> $ :List of 2
-#> ..$ alpha: num -1.41
-#> ..$ beta : num [1:3] 0.143 -1.043 0.9
+#> ..$ alpha: num -1.4
+#> ..$ beta : num [1:3] -1.313 -1.04 0.224
# partial inits (only specifying for a subset of parameters)
init_list <- list(
diff --git a/docs/reference/fit-method-inv_metric.html b/docs/reference/fit-method-inv_metric.html
index feac9970..3705aecd 100644
--- a/docs/reference/fit-method-inv_metric.html
+++ b/docs/reference/fit-method-inv_metric.html
@@ -140,75 +140,75 @@ Examples
fit$inv_metric()
#> $`1`
#> [,1] [,2] [,3] [,4]
-#> [1,] 0.0461164 0.0000000 0.0000000 0.0000000
-#> [2,] 0.0000000 0.0586249 0.0000000 0.0000000
-#> [3,] 0.0000000 0.0000000 0.0569083 0.0000000
-#> [4,] 0.0000000 0.0000000 0.0000000 0.0695349
+#> [1,] 0.0435508 0.0000000 0.0000000 0.0000000
+#> [2,] 0.0000000 0.0607469 0.0000000 0.0000000
+#> [3,] 0.0000000 0.0000000 0.0503494 0.0000000
+#> [4,] 0.0000000 0.0000000 0.0000000 0.0672705
#>
#> $`2`
#> [,1] [,2] [,3] [,4]
-#> [1,] 0.0423827 0.0000000 0.0000000 0.0000000
-#> [2,] 0.0000000 0.0638639 0.0000000 0.0000000
-#> [3,] 0.0000000 0.0000000 0.0463094 0.0000000
-#> [4,] 0.0000000 0.0000000 0.0000000 0.0646605
+#> [1,] 0.0532065 0.0000000 0.0000000 0.0000000
+#> [2,] 0.0000000 0.0574785 0.0000000 0.0000000
+#> [3,] 0.0000000 0.0000000 0.0512835 0.0000000
+#> [4,] 0.0000000 0.0000000 0.0000000 0.0641874
#>
#> $`3`
#> [,1] [,2] [,3] [,4]
-#> [1,] 0.0555959 0.0000000 0.0000000 0.0000000
-#> [2,] 0.0000000 0.0595803 0.0000000 0.0000000
-#> [3,] 0.0000000 0.0000000 0.0515264 0.0000000
-#> [4,] 0.0000000 0.0000000 0.0000000 0.0759908
+#> [1,] 0.0502858 0.0000000 0.0000000 0.0000000
+#> [2,] 0.0000000 0.0629888 0.0000000 0.0000000
+#> [3,] 0.0000000 0.0000000 0.0512933 0.0000000
+#> [4,] 0.0000000 0.0000000 0.0000000 0.0661664
#>
#> $`4`
-#> [,1] [,2] [,3] [,4]
-#> [1,] 0.05104 0.000000 0.0000000 0.00000
-#> [2,] 0.00000 0.051252 0.0000000 0.00000
-#> [3,] 0.00000 0.000000 0.0567318 0.00000
-#> [4,] 0.00000 0.000000 0.0000000 0.06699
+#> [,1] [,2] [,3] [,4]
+#> [1,] 0.0453724 0.000000 0.00000 0.0000000
+#> [2,] 0.0000000 0.061371 0.00000 0.0000000
+#> [3,] 0.0000000 0.000000 0.04017 0.0000000
+#> [4,] 0.0000000 0.000000 0.00000 0.0682251
#>
fit$inv_metric(matrix=FALSE)
#> $`1`
-#> [1] 0.0461164 0.0586249 0.0569083 0.0695349
+#> [1] 0.0435508 0.0607469 0.0503494 0.0672705
#>
#> $`2`
-#> [1] 0.0423827 0.0638639 0.0463094 0.0646605
+#> [1] 0.0532065 0.0574785 0.0512835 0.0641874
#>
#> $`3`
-#> [1] 0.0555959 0.0595803 0.0515264 0.0759908
+#> [1] 0.0502858 0.0629888 0.0512933 0.0661664
#>
#> $`4`
-#> [1] 0.0510400 0.0512520 0.0567318 0.0669900
+#> [1] 0.0453724 0.0613710 0.0401700 0.0682251
#>
fit <- cmdstanr_example("logistic", metric = "dense_e")
fit$inv_metric()
#> $`1`
-#> [,1] [,2] [,3] [,4]
-#> [1,] 0.048223400 -0.00737038 0.000297676 0.00680596
-#> [2,] -0.007370380 0.05440320 -0.006681250 -0.00162053
-#> [3,] 0.000297676 -0.00668125 0.043576600 -0.00859282
-#> [4,] 0.006805960 -0.00162053 -0.008592820 0.06856220
+#> [,1] [,2] [,3] [,4]
+#> [1,] 0.04487240 -0.00665557 0.00248036 -0.00336473
+#> [2,] -0.00665557 0.05394230 -0.00841737 -0.00815855
+#> [3,] 0.00248036 -0.00841737 0.04945140 -0.01015960
+#> [4,] -0.00336473 -0.00815855 -0.01015960 0.07289150
#>
#> $`2`
-#> [,1] [,2] [,3] [,4]
-#> [1,] 0.04746980 -0.00175084 -0.00118626 0.00664285
-#> [2,] -0.00175084 0.06320920 -0.00172180 -0.01259270
-#> [3,] -0.00118626 -0.00172180 0.04810590 -0.00872089
-#> [4,] 0.00664285 -0.01259270 -0.00872089 0.06689500
+#> [,1] [,2] [,3] [,4]
+#> [1,] 0.044891100 -0.00756532 0.00352846 -0.000462971
+#> [2,] -0.007565320 0.06064960 -0.00809279 -0.004813840
+#> [3,] 0.003528460 -0.00809279 0.05892730 -0.016947100
+#> [4,] -0.000462971 -0.00481384 -0.01694710 0.063878300
#>
#> $`3`
-#> [,1] [,2] [,3] [,4]
-#> [1,] 0.04482880 -0.00377658 -0.00343049 0.00304829
-#> [2,] -0.00377658 0.04803130 -0.00957526 -0.00566723
-#> [3,] -0.00343049 -0.00957526 0.05099840 -0.00574551
-#> [4,] 0.00304829 -0.00566723 -0.00574551 0.07092750
+#> [,1] [,2] [,3] [,4]
+#> [1,] 0.047806100 0.000761202 0.00579535 0.00494546
+#> [2,] 0.000761202 0.062074500 -0.00488086 -0.01014510
+#> [3,] 0.005795350 -0.004880860 0.05170230 -0.00609765
+#> [4,] 0.004945460 -0.010145100 -0.00609765 0.07081120
#>
#> $`4`
-#> [,1] [,2] [,3] [,4]
-#> [1,] 0.04787540 -0.003918010 -0.004273680 0.00253868
-#> [2,] -0.00391801 0.049792000 0.000712128 -0.00453177
-#> [3,] -0.00427368 0.000712128 0.045850800 -0.00639672
-#> [4,] 0.00253868 -0.004531770 -0.006396720 0.07511110
+#> [,1] [,2] [,3] [,4]
+#> [1,] 0.04387350 0.004639300 0.001929550 0.00526756
+#> [2,] 0.00463930 0.064076200 -0.000902685 -0.00609741
+#> [3,] 0.00192955 -0.000902685 0.045911200 -0.00859578
+#> [4,] 0.00526756 -0.006097410 -0.008595780 0.07087270
#>
# }
diff --git a/docs/reference/fit-method-log_prob.html b/docs/reference/fit-method-log_prob.html
index a0a9f638..561ca052 100644
--- a/docs/reference/fit-method-log_prob.html
+++ b/docs/reference/fit-method-log_prob.html
@@ -1,5 +1,6 @@
-Calculate the log-probability given a provided vector of unconstrained parameters. — fit-method-log_prob • cmdstanr Calculate the log-probability given a provided vector of unconstrained parameters. — fit-method-log_prob • cmdstanr
@@ -104,23 +105,27 @@ Calculate the log-probability given a provided vector of unconstrained param
- The $log_prob() method provides access to the Stan model's log_prob function
+ The $log_prob() method provides access to the Stan model's
+log_prob function.
- log_prob(unconstrained_variables, jacobian_adjustment = TRUE)
+ log_prob(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = NULL)
Arguments
- unconstrained_variables
-(numeric) A vector of unconstrained parameters
-to be passed to log_prob.
(numeric) A vector of unconstrained parameters.
- jacobian
+(logical) Whether to include the log-density adjustments from
+un/constraining variables.
- jacobian_adjustment
-(logical) Whether to include the log-density
-adjustments from un/constraining variables.
Deprecated. Please use jacobian instead.
diff --git a/docs/reference/fit-method-loo.html b/docs/reference/fit-method-loo.html
index 5a1a1743..54c58466 100644
--- a/docs/reference/fit-method-loo.html
+++ b/docs/reference/fit-method-loo.html
@@ -181,9 +181,9 @@ Examples
#> Computed from 4000 by 100 log-likelihood matrix
#>
#> Estimate SE
-#> elpd_loo -63.7 4.2
+#> elpd_loo -63.7 4.1
#> p_loo 3.9 0.5
-#> looic 127.3 8.3
+#> looic 127.4 8.2
#> ------
#> Monte Carlo SE of elpd_loo is 0.0.
#>
diff --git a/docs/reference/fit-method-lp-1.png b/docs/reference/fit-method-lp-1.png
index 508c1b84..980bd46b 100644
Binary files a/docs/reference/fit-method-lp-1.png and b/docs/reference/fit-method-lp-1.png differ
diff --git a/docs/reference/fit-method-lp.html b/docs/reference/fit-method-lp.html
index 1dd19bc7..bca9a329 100644
--- a/docs/reference/fit-method-lp.html
+++ b/docs/reference/fit-method-lp.html
@@ -170,7 +170,7 @@ Examples
# \dontrun{
fit_mcmc <- cmdstanr_example("logistic")
head(fit_mcmc$lp())
-#> [1] -65.4484 -67.4196 -65.8307 -65.3367 -67.3193 -66.8288
+#> [1] -65.6405 -64.4734 -65.4431 -64.9703 -65.8088 -65.6905
fit_mle <- cmdstanr_example("logistic", method = "optimize")
fit_mle$lp()
diff --git a/docs/reference/fit-method-metadata.html b/docs/reference/fit-method-metadata.html
index f97d066f..01634904 100644
--- a/docs/reference/fit-method-metadata.html
+++ b/docs/reference/fit-method-metadata.html
@@ -129,7 +129,7 @@ Examples
#> $ stan_version_major : num 2
#> $ stan_version_minor : num 33
#> $ stan_version_patch : num 0
-#> $ start_datetime : chr "2023-11-08 17:29:44 UTC"
+#> $ start_datetime : chr "2023-12-13 17:04:42 UTC"
#> $ method : chr "sample"
#> $ save_warmup : num 0
#> $ thin : num 1
@@ -146,14 +146,14 @@ Examples
#> $ num_chains : num 1
#> $ id : num [1:4] 1 2 3 4
#> $ init : num [1:4] 2 2 2 2
-#> $ seed : num 1.39e+09
+#> $ seed : num 1.08e+09
#> $ refresh : num 100
#> $ sig_figs : num -1
-#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-216cf7.csv"
+#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-79f552.csv"
#> $ stanc_version : chr "stanc3 v2.33.1"
#> $ sampler_diagnostics : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ...
#> $ variables : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
-#> $ step_size_adaptation: num [1:4] 0.779 0.737 0.698 0.697
+#> $ step_size_adaptation: num [1:4] 0.723 0.882 0.802 0.749
#> $ model_name : chr "logistic_model"
#> $ adapt_engaged : num 1
#> $ adapt_delta : num 0.8
@@ -164,9 +164,9 @@ Examples
#> $ threads_per_chain : num 1
#> $ time :'data.frame': 4 obs. of 4 variables:
#> ..$ chain_id: num [1:4] 1 2 3 4
-#> ..$ warmup : num [1:4] 0.022 0.023 0.023 0.023
-#> ..$ sampling: num [1:4] 0.081 0.074 0.083 0.081
-#> ..$ total : num [1:4] 0.103 0.097 0.106 0.104
+#> ..$ warmup : num [1:4] 0.022 0.022 0.022 0.022
+#> ..$ sampling: num [1:4] 0.069 0.065 0.07 0.068
+#> ..$ total : num [1:4] 0.091 0.087 0.092 0.09
#> $ stan_variable_sizes :List of 4
#> ..$ lp__ : num 1
#> ..$ alpha : num 1
@@ -181,7 +181,7 @@ Examples
#> $ stan_version_major : num 2
#> $ stan_version_minor : num 33
#> $ stan_version_patch : num 0
-#> $ start_datetime : chr "2023-11-08 17:29:46 UTC"
+#> $ start_datetime : chr "2023-12-13 17:04:44 UTC"
#> $ method : chr "optimize"
#> $ algorithm : chr "lbfgs"
#> $ init_alpha : num 0.001
@@ -196,10 +196,10 @@ Examples
#> $ save_iterations : num 0
#> $ id : num 1
#> $ init : num 2
-#> $ seed : num 1.28e+09
+#> $ seed : num 9.12e+08
#> $ refresh : num 100
#> $ sig_figs : num -1
-#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-65f6b8.csv"
+#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-7b3d9a.csv"
#> $ stanc_version : chr "stanc3 v2.33.1"
#> $ sampler_diagnostics: chr(0)
#> $ variables : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
@@ -220,7 +220,7 @@ Examples
#> $ stan_version_major : num 2
#> $ stan_version_minor : num 33
#> $ stan_version_patch : num 0
-#> $ start_datetime : chr "2023-11-08 17:29:46 UTC"
+#> $ start_datetime : chr "2023-12-13 17:04:44 UTC"
#> $ method : chr "variational"
#> $ algorithm : chr "meanfield"
#> $ iter : num 50
@@ -232,10 +232,10 @@ Examples
#> $ output_samples : num 1000
#> $ id : num 1
#> $ init : num 2
-#> $ seed : num 9.18e+08
+#> $ seed : num 3.89e+08
#> $ refresh : num 100
#> $ sig_figs : num -1
-#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-40a7a2.csv"
+#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-645608.csv"
#> $ stanc_version : chr "stanc3 v2.33.1"
#> $ sampler_diagnostics: chr(0)
#> $ variables : chr [1:106] "lp__" "lp_approx__" "alpha" "beta[1]" ...
diff --git a/docs/reference/fit-method-mle.html b/docs/reference/fit-method-mle.html
index 53b88a5e..c9ac5821 100644
--- a/docs/reference/fit-method-mle.html
+++ b/docs/reference/fit-method-mle.html
@@ -147,14 +147,14 @@ Examples
# \dontrun{
fit <- cmdstanr_example("logistic", method = "optimize")
fit$mle("alpha")
-#> alpha
-#> 0.36447
+#> alpha
+#> 0.364472
fit$mle("beta")
#> beta[1] beta[2] beta[3]
-#> -0.631542 -0.258968 0.648496
+#> -0.631560 -0.258925 0.648500
fit$mle("beta[2]")
#> beta[2]
-#> -0.258968
+#> -0.258925
# }
diff --git a/docs/reference/fit-method-output.html b/docs/reference/fit-method-output.html
index d981dc43..ba2dc64f 100644
--- a/docs/reference/fit-method-output.html
+++ b/docs/reference/fit-method-output.html
@@ -166,21 +166,21 @@ Examples
#> num_chains = 1 (Default)
#> id = 1 (Default)
#> data
-#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpYtRZP3/temp_libpath119dc23a21f00/cmdstanr/logistic.data.json
+#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmps9TW4l/temp_libpath5800f91909/cmdstanr/logistic.data.json
#> init = 2 (Default)
#> random
-#> seed = 1225363303
+#> seed = 1467887685
#> output
-#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081029-1-2c7d41.csv
+#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131004-1-910e87.csv
#> diagnostic_file = (Default)
#> refresh = 100 (Default)
#> sig_figs = -1 (Default)
-#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-1ddd0e.csv
+#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-90a553.csv
#> num_threads = 1 (Default)
#>
#>
-#> Gradient evaluation took 2.2e-05 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
+#> Gradient evaluation took 1.9e-05 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Adjust your expectations accordingly!
#>
#>
@@ -207,9 +207,9 @@ Examples
#> Iteration: 1900 / 2000 [ 95%] (Sampling)
#> Iteration: 2000 / 2000 [100%] (Sampling)
#>
-#> Elapsed Time: 0.025 seconds (Warm-up)
-#> 0.082 seconds (Sampling)
-#> 0.107 seconds (Total)
+#> Elapsed Time: 0.022 seconds (Warm-up)
+#> 0.072 seconds (Sampling)
+#> 0.094 seconds (Total)
out <- fit_mcmc$output()
str(out)
#> List of 4
@@ -237,21 +237,21 @@ Examples
#> save_iterations = 0 (Default)
#> id = 1 (Default)
#> data
-#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpYtRZP3/temp_libpath119dc23a21f00/cmdstanr/logistic.data.json
+#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmps9TW4l/temp_libpath5800f91909/cmdstanr/logistic.data.json
#> init = 2 (Default)
#> random
-#> seed = 152179067
+#> seed = 44204223
#> output
-#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081029-1-0168cf.csv
+#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131004-1-75dd88.csv
#> diagnostic_file = (Default)
#> refresh = 100 (Default)
#> sig_figs = -1 (Default)
-#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-216c50.csv
+#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-5c2d53.csv
#> num_threads = 1 (Default)
#>
-#> Initial log joint probability = -95.9632
+#> Initial log joint probability = -80.1217
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 7 -63.9218 0.000242271 7.03951e-05 1 1 9
+#> 7 -63.9218 0.00150318 0.00093398 1 1 9
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
@@ -274,16 +274,16 @@ Examples
#> output_samples = 1000 (Default)
#> id = 1 (Default)
#> data
-#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpYtRZP3/temp_libpath119dc23a21f00/cmdstanr/logistic.data.json
+#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmps9TW4l/temp_libpath5800f91909/cmdstanr/logistic.data.json
#> init = 2 (Default)
#> random
-#> seed = 676659604
+#> seed = 244304498
#> output
-#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081029-1-6087e5.csv
+#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131004-1-3b4b11.csv
#> diagnostic_file = (Default)
#> refresh = 100 (Default)
#> sig_figs = -1 (Default)
-#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-8ae7e6.csv
+#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-85d291.csv
#> num_threads = 1 (Default)
#>
#> ------------------------------------------------------------
@@ -294,8 +294,8 @@ Examples
#>
#>
#>
-#> Gradient evaluation took 1.8e-05 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
+#> Gradient evaluation took 1.9e-05 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Adjust your expectations accordingly!
#>
#>
@@ -309,9 +309,9 @@ Examples
#>
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
-#> 100 -66.040 1.000 1.000
-#> 200 -66.445 0.503 1.000
-#> 300 -66.355 0.336 0.006 MEDIAN ELBO CONVERGED
+#> 100 -66.299 1.000 1.000
+#> 200 -66.336 0.500 1.000
+#> 300 -66.628 0.335 0.004 MEDIAN ELBO CONVERGED
#>
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
diff --git a/docs/reference/fit-method-profiles.html b/docs/reference/fit-method-profiles.html
index 6d97a13a..7c4e4b8c 100644
--- a/docs/reference/fit-method-profiles.html
+++ b/docs/reference/fit-method-profiles.html
@@ -176,32 +176,32 @@ Examples
fit$profiles()
#> [[1]]
#> name thread_id total_time forward_time reverse_time chain_stack
-#> 1 gq 0x1061d4e00 0.000364609 0.000364609 0.000000000 0
-#> 2 likelihood 0x1061d4e00 0.001171810 0.000840557 0.000331257 7169
+#> 1 gq 0x10efcde00 0.000375429 0.000375429 0.000000000 0
+#> 2 likelihood 0x10efcde00 0.001165040 0.000834157 0.000330887 7169
#> no_chain_stack autodiff_calls no_autodiff_calls
#> 1 0 0 1000
#> 2 7169 7169 1
#>
#> [[2]]
#> name thread_id total_time forward_time reverse_time chain_stack
-#> 1 gq 0x10e36de00 0.000346564 0.000346564 0.000000000 0
-#> 2 likelihood 0x10e36de00 0.001186840 0.000842553 0.000344283 7155
+#> 1 gq 0x10d0b0e00 0.000407951 0.000407951 0.000000000 0
+#> 2 likelihood 0x10d0b0e00 0.001283690 0.000915781 0.000367905 7155
#> no_chain_stack autodiff_calls no_autodiff_calls
#> 1 0 0 1000
#> 2 7155 7155 1
#>
#> [[3]]
#> name thread_id total_time forward_time reverse_time chain_stack
-#> 1 gq 0x111ab6e00 0.000351792 0.000351792 0.000000000 0
-#> 2 likelihood 0x111ab6e00 0.001143140 0.000829663 0.000313482 6879
+#> 1 gq 0x106bb1e00 0.000399315 0.000399315 0.000000000 0
+#> 2 likelihood 0x106bb1e00 0.001242900 0.000887379 0.000355522 6879
#> no_chain_stack autodiff_calls no_autodiff_calls
#> 1 0 0 1000
#> 2 6879 6879 1
#>
#> [[4]]
#> name thread_id total_time forward_time reverse_time chain_stack
-#> 1 gq 0x10c867e00 0.000408638 0.000408638 0.000000000 0
-#> 2 likelihood 0x10c867e00 0.001279700 0.000924260 0.000355437 6892
+#> 1 gq 0x111b21e00 0.000516567 0.000516567 0.000000000 0
+#> 2 likelihood 0x111b21e00 0.001479350 0.001061390 0.000417962 6892
#> no_chain_stack autodiff_calls no_autodiff_calls
#> 1 0 0 1000
#> 2 6892 6892 1
diff --git a/docs/reference/fit-method-return_codes.html b/docs/reference/fit-method-return_codes.html
index 734fbae7..83a654e0 100644
--- a/docs/reference/fit-method-return_codes.html
+++ b/docs/reference/fit-method-return_codes.html
@@ -130,9 +130,7 @@ Examples
# \dontrun{
# example with return codes all zero
fit_mcmc <- cmdstanr_example("schools", method = "sample")
-#> Warning: 238 of 4000 (6.0%) transitions ended with a divergence.
-#> See https://mc-stan.org/misc/warnings for details.
-#> Warning: 1 of 4 chains had an E-BFMI less than 0.2.
+#> Warning: 72 of 4000 (2.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.
fit_mcmc$return_codes() # should be all zero
#> [1] 0 0 0 0
diff --git a/docs/reference/fit-method-sampler_diagnostics.html b/docs/reference/fit-method-sampler_diagnostics.html
index e83c1bf5..69c92928 100644
--- a/docs/reference/fit-method-sampler_diagnostics.html
+++ b/docs/reference/fit-method-sampler_diagnostics.html
@@ -152,7 +152,7 @@ Examples
fit <- cmdstanr_example("logistic")
sampler_diagnostics <- fit$sampler_diagnostics()
str(sampler_diagnostics)
-#> 'draws_array' num [1:1000, 1:4, 1:6] 2 2 2 2 2 3 2 2 2 3 ...
+#> 'draws_array' num [1:1000, 1:4, 1:6] 2 2 2 2 3 2 2 3 2 2 ...
#> - attr(*, "dimnames")=List of 3
#> ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
#> ..$ chain : chr [1:4] "1" "2" "3" "4"
@@ -162,16 +162,16 @@ Examples
as_draws_df(sampler_diagnostics)
#> # A draws_df: 1000 iterations, 4 chains, and 6 variables
#> treedepth__ divergent__ energy__ accept_stat__ stepsize__ n_leapfrog__
-#> 1 2 0 70 0.76 0.73 3
-#> 2 2 0 69 1.00 0.73 3
-#> 3 2 0 69 1.00 0.73 3
-#> 4 2 0 66 1.00 0.73 3
-#> 5 2 0 66 0.97 0.73 3
-#> 6 3 0 67 0.88 0.73 7
-#> 7 2 0 72 0.74 0.73 3
-#> 8 2 0 73 1.00 0.73 3
-#> 9 2 0 69 1.00 0.73 3
-#> 10 3 0 66 0.95 0.73 7
+#> 1 2 0 66 0.87 0.85 3
+#> 2 2 0 66 0.91 0.85 3
+#> 3 2 0 66 0.99 0.85 3
+#> 4 2 0 65 1.00 0.85 3
+#> 5 3 0 65 0.94 0.85 7
+#> 6 2 0 68 0.83 0.85 7
+#> 7 2 0 66 0.93 0.85 7
+#> 8 3 0 67 0.94 0.85 7
+#> 9 2 0 66 1.00 0.85 3
+#> 10 2 0 68 0.65 0.85 3
#> # ... with 3990 more draws
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
@@ -179,16 +179,16 @@ Examples
fit$sampler_diagnostics(format = "df")
#> # A draws_df: 1000 iterations, 4 chains, and 6 variables
#> treedepth__ divergent__ energy__ accept_stat__ stepsize__ n_leapfrog__
-#> 1 2 0 70 0.76 0.73 3
-#> 2 2 0 69 1.00 0.73 3
-#> 3 2 0 69 1.00 0.73 3
-#> 4 2 0 66 1.00 0.73 3
-#> 5 2 0 66 0.97 0.73 3
-#> 6 3 0 67 0.88 0.73 7
-#> 7 2 0 72 0.74 0.73 3
-#> 8 2 0 73 1.00 0.73 3
-#> 9 2 0 69 1.00 0.73 3
-#> 10 3 0 66 0.95 0.73 7
+#> 1 2 0 66 0.87 0.85 3
+#> 2 2 0 66 0.91 0.85 3
+#> 3 2 0 66 0.99 0.85 3
+#> 4 2 0 65 1.00 0.85 3
+#> 5 3 0 65 0.94 0.85 7
+#> 6 2 0 68 0.83 0.85 7
+#> 7 2 0 66 0.93 0.85 7
+#> 8 3 0 67 0.94 0.85 7
+#> 9 2 0 66 1.00 0.85 3
+#> 10 2 0 68 0.65 0.85 3
#> # ... with 3990 more draws
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
# }
diff --git a/docs/reference/fit-method-save_object.html b/docs/reference/fit-method-save_object.html
index 90155c94..8464814f 100644
--- a/docs/reference/fit-method-save_object.html
+++ b/docs/reference/fit-method-save_object.html
@@ -148,16 +148,16 @@ Examples
#> # A tibble: 105 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -66.0 -65.6 1.43 1.24 -68.6 -64.3 1.00 2220.
-#> 2 alpha 0.383 0.380 0.214 0.213 0.0497 0.741 1.00 4650.
-#> 3 beta[1] -0.669 -0.668 0.251 0.258 -1.08 -0.266 1.00 4431.
-#> 4 beta[2] -0.274 -0.269 0.226 0.224 -0.644 0.0995 1.00 3964.
-#> 5 beta[3] 0.675 0.670 0.269 0.269 0.243 1.13 1.00 3857.
-#> 6 log_lik[1] -0.513 -0.506 0.0969 0.0968 -0.678 -0.368 1.00 4700.
-#> 7 log_lik[2] -0.407 -0.386 0.151 0.144 -0.689 -0.194 1.00 4212.
-#> 8 log_lik[3] -0.496 -0.460 0.215 0.203 -0.888 -0.211 1.00 4110.
-#> 9 log_lik[4] -0.452 -0.436 0.152 0.146 -0.732 -0.233 1.00 4273.
-#> 10 log_lik[5] -1.18 -1.16 0.274 0.268 -1.65 -0.760 1.00 4342.
+#> 1 lp__ -65.9 -65.6 1.40 1.17 -68.7 -64.3 1.00 1893.
+#> 2 alpha 0.380 0.380 0.218 0.216 0.0231 0.740 1.00 4801.
+#> 3 beta[1] -0.668 -0.659 0.248 0.247 -1.09 -0.273 1.00 4573.
+#> 4 beta[2] -0.271 -0.267 0.224 0.229 -0.634 0.0845 1.00 4645.
+#> 5 beta[3] 0.678 0.666 0.266 0.259 0.248 1.12 1.00 4505.
+#> 6 log_lik[1] -0.515 -0.508 0.0990 0.0965 -0.689 -0.362 1.00 4844.
+#> 7 log_lik[2] -0.403 -0.384 0.147 0.139 -0.669 -0.199 1.00 4585.
+#> 8 log_lik[3] -0.495 -0.459 0.218 0.202 -0.898 -0.206 1.00 4890.
+#> 9 log_lik[4] -0.452 -0.433 0.153 0.150 -0.726 -0.239 1.00 4491.
+#> 10 log_lik[5] -1.18 -1.16 0.278 0.275 -1.67 -0.765 1.00 5130.
#> # ℹ 95 more rows
#> # ℹ 1 more variable: ess_tail <dbl>
# }
diff --git a/docs/reference/fit-method-save_output_files.html b/docs/reference/fit-method-save_output_files.html
index f096f70e..8fb2b772 100644
--- a/docs/reference/fit-method-save_output_files.html
+++ b/docs/reference/fit-method-save_output_files.html
@@ -210,33 +210,33 @@ Examples
# \dontrun{
fit <- cmdstanr_example()
fit$output_files()
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081030-1-98eced.csv"
-#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081030-2-98eced.csv"
-#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081030-3-98eced.csv"
-#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081030-4-98eced.csv"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131005-1-212881.csv"
+#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131005-2-212881.csv"
+#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131005-3-212881.csv"
+#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131005-4-212881.csv"
fit$data_file()
-#> [1] "/private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpYtRZP3/temp_libpath119dc23a21f00/cmdstanr/logistic.data.json"
+#> [1] "/private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmps9TW4l/temp_libpath5800f91909/cmdstanr/logistic.data.json"
# just using tempdir for the example
my_dir <- tempdir()
fit$save_output_files(dir = my_dir, basename = "banana")
#> Moved 4 files and set internal paths to new locations:
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/banana-202311081030-1-846feb.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/banana-202311081030-2-846feb.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/banana-202311081030-3-846feb.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/banana-202311081030-4-846feb.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/banana-202312131005-1-932dd0.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/banana-202312131005-2-932dd0.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/banana-202312131005-3-932dd0.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/banana-202312131005-4-932dd0.csv
fit$save_output_files(dir = my_dir, basename = "tomato", timestamp = FALSE)
#> Moved 4 files and set internal paths to new locations:
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/tomato-1-270839.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/tomato-2-270839.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/tomato-3-270839.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/tomato-4-270839.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/tomato-1-1f5444.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/tomato-2-1f5444.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/tomato-3-1f5444.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/tomato-4-1f5444.csv
fit$save_output_files(dir = my_dir, basename = "lettuce", timestamp = FALSE, random = FALSE)
#> Moved 4 files and set internal paths to new locations:
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/lettuce-1.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/lettuce-2.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/lettuce-3.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/lettuce-4.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/lettuce-1.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/lettuce-2.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/lettuce-3.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/lettuce-4.csv
# }
diff --git a/docs/reference/fit-method-summary.html b/docs/reference/fit-method-summary.html
index 6f8199fa..14110b92 100644
--- a/docs/reference/fit-method-summary.html
+++ b/docs/reference/fit-method-summary.html
@@ -165,36 +165,36 @@ Examples
#> # A tibble: 105 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -65.9 -65.6 1.43 1.23 -68.7 -64.3 1.00 2038.
-#> 2 alpha 0.376 0.369 0.215 0.214 0.0214 0.727 1.00 4221.
-#> 3 beta[1] -0.665 -0.659 0.247 0.241 -1.08 -0.263 1.00 4093.
-#> 4 beta[2] -0.275 -0.271 0.224 0.221 -0.652 0.0850 1.00 4052.
-#> 5 beta[3] 0.676 0.669 0.268 0.268 0.244 1.12 1.00 4213.
-#> 6 log_lik[1] -0.517 -0.511 0.0979 0.0959 -0.683 -0.369 1.00 4266.
-#> 7 log_lik[2] -0.404 -0.383 0.147 0.140 -0.669 -0.200 1.00 4613.
-#> 8 log_lik[3] -0.501 -0.466 0.218 0.200 -0.913 -0.209 1.00 4197.
-#> 9 log_lik[4] -0.450 -0.430 0.153 0.145 -0.724 -0.229 1.00 4069.
-#> 10 log_lik[5] -1.18 -1.15 0.278 0.270 -1.67 -0.758 1.00 4386.
+#> 1 lp__ -66.0 -65.7 1.48 1.23 -68.9 -64.3 1.00 1956.
+#> 2 alpha 0.382 0.381 0.221 0.222 0.0263 0.744 1.00 3982.
+#> 3 beta[1] -0.665 -0.661 0.252 0.253 -1.09 -0.264 1.00 4131.
+#> 4 beta[2] -0.274 -0.280 0.226 0.226 -0.640 0.0976 1.00 3816.
+#> 5 beta[3] 0.675 0.673 0.268 0.265 0.238 1.12 1.00 3963.
+#> 6 log_lik[1] -0.514 -0.508 0.0987 0.0980 -0.686 -0.364 1.00 3980.
+#> 7 log_lik[2] -0.408 -0.385 0.153 0.142 -0.690 -0.197 1.00 4263.
+#> 8 log_lik[3] -0.498 -0.467 0.217 0.204 -0.889 -0.206 1.00 3904.
+#> 9 log_lik[4] -0.453 -0.436 0.152 0.148 -0.732 -0.236 1.00 3847.
+#> 10 log_lik[5] -1.18 -1.16 0.285 0.281 -1.68 -0.749 1.00 4407.
#> # ℹ 95 more rows
#> # ℹ 1 more variable: ess_tail <dbl>
fit$print()
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
-#> lp__ -65.92 -65.59 1.43 1.23 -68.71 -64.30 1.00 2038 2638
-#> alpha 0.38 0.37 0.21 0.21 0.02 0.73 1.00 4221 3302
-#> beta[1] -0.67 -0.66 0.25 0.24 -1.08 -0.26 1.00 4092 2987
-#> beta[2] -0.28 -0.27 0.22 0.22 -0.65 0.09 1.00 4052 3349
-#> beta[3] 0.68 0.67 0.27 0.27 0.24 1.12 1.00 4212 2892
-#> log_lik[1] -0.52 -0.51 0.10 0.10 -0.68 -0.37 1.00 4265 3448
-#> log_lik[2] -0.40 -0.38 0.15 0.14 -0.67 -0.20 1.00 4612 3246
-#> log_lik[3] -0.50 -0.47 0.22 0.20 -0.91 -0.21 1.00 4197 3040
-#> log_lik[4] -0.45 -0.43 0.15 0.14 -0.72 -0.23 1.00 4069 2989
-#> log_lik[5] -1.18 -1.15 0.28 0.27 -1.67 -0.76 1.00 4386 2650
+#> lp__ -66.00 -65.68 1.48 1.23 -68.89 -64.30 1.00 1956 2665
+#> alpha 0.38 0.38 0.22 0.22 0.03 0.74 1.00 3981 3124
+#> beta[1] -0.66 -0.66 0.25 0.25 -1.09 -0.26 1.00 4131 3206
+#> beta[2] -0.27 -0.28 0.23 0.23 -0.64 0.10 1.00 3815 2736
+#> beta[3] 0.67 0.67 0.27 0.26 0.24 1.12 1.00 3963 3257
+#> log_lik[1] -0.51 -0.51 0.10 0.10 -0.69 -0.36 1.00 3979 2841
+#> log_lik[2] -0.41 -0.39 0.15 0.14 -0.69 -0.20 1.00 4263 3051
+#> log_lik[3] -0.50 -0.47 0.22 0.20 -0.89 -0.21 1.00 3904 3141
+#> log_lik[4] -0.45 -0.44 0.15 0.15 -0.73 -0.24 1.00 3847 2904
+#> log_lik[5] -1.18 -1.16 0.28 0.28 -1.68 -0.75 1.00 4406 3095
#>
#> # showing 10 of 105 rows (change via 'max_rows' argument or 'cmdstanr_max_rows' option)
fit$print(max_rows = 2) # same as print(fit, max_rows = 2)
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
-#> lp__ -65.92 -65.59 1.43 1.23 -68.71 -64.30 1.00 2038 2638
-#> alpha 0.38 0.37 0.21 0.21 0.02 0.73 1.00 4221 3302
+#> lp__ -66.00 -65.68 1.48 1.23 -68.89 -64.30 1.00 1956 2665
+#> alpha 0.38 0.38 0.22 0.22 0.03 0.74 1.00 3981 3124
#>
#> # showing 2 of 105 rows (change via 'max_rows' argument or 'cmdstanr_max_rows' option)
@@ -203,29 +203,29 @@ Examples
#> # A tibble: 3 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 beta[1] -0.665 -0.659 0.247 0.241 -1.08 -0.263 1.00 4093. 2988.
-#> 2 beta[2] -0.275 -0.271 0.224 0.221 -0.652 0.0850 1.00 4052. 3350.
-#> 3 beta[3] 0.676 0.669 0.268 0.268 0.244 1.12 1.00 4213. 2892.
+#> 1 beta[1] -0.665 -0.661 0.252 0.253 -1.09 -0.264 1.00 4131. 3207.
+#> 2 beta[2] -0.274 -0.280 0.226 0.226 -0.640 0.0976 1.00 3816. 2736.
+#> 3 beta[3] 0.675 0.673 0.268 0.265 0.238 1.12 1.00 3963. 3257.
fit$print(c("alpha", "beta[2]"))
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
-#> alpha 0.38 0.37 0.21 0.21 0.02 0.73 1.00 4221 3302
-#> beta[2] -0.28 -0.27 0.22 0.22 -0.65 0.09 1.00 4052 3349
+#> alpha 0.38 0.38 0.22 0.22 0.03 0.74 1.00 3981 3124
+#> beta[2] -0.27 -0.28 0.23 0.23 -0.64 0.10 1.00 3815 2736
# include all variables but only certain summaries
fit$summary(NULL, c("mean", "sd"))
#> # A tibble: 105 × 3
#> variable mean sd
#> <chr> <dbl> <dbl>
-#> 1 lp__ -65.9 1.43
-#> 2 alpha 0.376 0.215
-#> 3 beta[1] -0.665 0.247
-#> 4 beta[2] -0.275 0.224
-#> 5 beta[3] 0.676 0.268
-#> 6 log_lik[1] -0.517 0.0979
-#> 7 log_lik[2] -0.404 0.147
-#> 8 log_lik[3] -0.501 0.218
-#> 9 log_lik[4] -0.450 0.153
-#> 10 log_lik[5] -1.18 0.278
+#> 1 lp__ -66.0 1.48
+#> 2 alpha 0.382 0.221
+#> 3 beta[1] -0.665 0.252
+#> 4 beta[2] -0.274 0.226
+#> 5 beta[3] 0.675 0.268
+#> 6 log_lik[1] -0.514 0.0987
+#> 7 log_lik[2] -0.408 0.153
+#> 8 log_lik[3] -0.498 0.217
+#> 9 log_lik[4] -0.453 0.152
+#> 10 log_lik[5] -1.18 0.285
#> # ℹ 95 more rows
# can use functions created from formulas
@@ -234,9 +234,9 @@ Examples
#> # A tibble: 3 × 2
#> variable prob_gt_0
#> <chr> <dbl>
-#> 1 beta[1] 0.00225
-#> 2 beta[2] 0.107
-#> 3 beta[3] 0.995
+#> 1 beta[1] 0.00325
+#> 2 beta[2] 0.114
+#> 3 beta[3] 0.997
# can combine user-specified functions with
# the default summary functions
@@ -248,10 +248,10 @@ Examples
#> # A tibble: 4 × 10
#> variable mean median sd mad q2.5 q97.5 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 alpha 0.376 0.369 0.215 0.214 -0.0365 0.800 1.00 4221. 3303.
-#> 2 beta[1] -0.665 -0.659 0.247 0.241 -1.17 -0.200 1.00 4093. 2988.
-#> 3 beta[2] -0.275 -0.271 0.224 0.221 -0.720 0.155 1.00 4052. 3350.
-#> 4 beta[3] 0.676 0.669 0.268 0.268 0.172 1.23 1.00 4213. 2892.
+#> 1 alpha 0.382 0.381 0.221 0.222 -0.0413 0.821 1.00 3982. 3124.
+#> 2 beta[1] -0.665 -0.661 0.252 0.253 -1.18 -0.185 1.00 4131. 3207.
+#> 3 beta[2] -0.274 -0.280 0.226 0.226 -0.715 0.168 1.00 3816. 2736.
+#> 4 beta[3] 0.675 0.673 0.268 0.265 0.170 1.20 1.00 3963. 3257.
# the functions need to calculate the appropriate
# value for a matrix input
diff --git a/docs/reference/fit-method-time.html b/docs/reference/fit-method-time.html
index 474e4a0b..c0a070b8 100644
--- a/docs/reference/fit-method-time.html
+++ b/docs/reference/fit-method-time.html
@@ -140,36 +140,36 @@ Examples
fit_mcmc <- cmdstanr_example("logistic", method = "sample")
fit_mcmc$time()
#> $total
-#> [1] 0.5498919
+#> [1] 0.554847
#>
#> $chains
#> chain_id warmup sampling total
#> 1 1 0.023 0.071 0.094
-#> 2 2 0.023 0.070 0.093
-#> 3 3 0.023 0.074 0.097
-#> 4 4 0.023 0.074 0.097
+#> 2 2 0.022 0.066 0.088
+#> 3 3 0.023 0.071 0.094
+#> 4 4 0.023 0.071 0.094
#>
fit_vb <- cmdstanr_example("logistic", method = "variational")
fit_vb$time()
#> $total
-#> [1] 0.1315289
+#> [1] 0.1302531
#>
fit_mle <- cmdstanr_example("logistic", method = "optimize", jacobian = TRUE)
fit_mle$time()
#> $total
-#> [1] 0.1302979
+#> [1] 0.1320548
#>
# use fit_mle to draw samples from laplace approximation
fit_laplace <- cmdstanr_example("logistic", method = "laplace", mode = fit_mle)
fit_laplace$time() # just time for drawing sample not for running optimize
#> $total
-#> [1] 0.136251
+#> [1] 0.126792
#>
fit_laplace$time()$total + fit_mle$time()$total # total time
-#> [1] 0.2665489
+#> [1] 0.2588468
# }
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 1036551c..c1a096fe 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -160,7 +160,7 @@ Running CmdStan from R laplace()
- Run Stan's laplace algorithm
Run Stan's Laplace algorithm
diff --git a/docs/reference/model-method-check_syntax.html b/docs/reference/model-method-check_syntax.html
index 14ad8c59..6ec6f59e 100644
--- a/docs/reference/model-method-check_syntax.html
+++ b/docs/reference/model-method-check_syntax.html
@@ -125,7 +125,7 @@ Arguments
- pedantic
(logical) Should pedantic mode be turned on? The default is
FALSE. Pedantic mode attempts to warn you about potential issues in your
-Stan program beyond syntax errors. For details see the Pedantic mode chapter in
+Stan program beyond syntax errors. For details see the Pedantic mode chapter in
the Stan Reference Manual.
Examples
# pedantic mode will warn that lambda should be constrained to be positive
# and that lambda has no prior distribution
mod$check_syntax(pedantic = TRUE)
-#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
+#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
#> poisson distribution is given parameter lambda as a rate parameter
#> (argument 1), but lambda was not constrained to be strictly positive.
#> Warning: The parameter lambda has no priors. This means either no prior is
diff --git a/docs/reference/model-method-compile.html b/docs/reference/model-method-compile.html
index 2ae3d9c3..e5a50034 100644
--- a/docs/reference/model-method-compile.html
+++ b/docs/reference/model-method-compile.html
@@ -171,7 +171,7 @@ Arguments
- pedantic
(logical) Should pedantic mode be turned on? The default is
FALSE. Pedantic mode attempts to warn you about potential issues in your
-Stan program beyond syntax errors. For details see the Pedantic mode chapter in
+Stan program beyond syntax errors. For details see the Pedantic mode chapter in
the Stan Reference Manual. Note: to do a pedantic check for a model
without compiling it or for a model that is already compiled the
$check_syntax() method can be used instead.
Examples
}
")
mod <- cmdstan_model(file_pedantic, pedantic = TRUE)
-#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model-11d996d1c906f.stan', line 6, column 2: Parameter
+#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/model-5f0d242e1d46.stan', line 6, column 2: Parameter
#> sigma is given a exponential distribution, which has strictly positive
#> support, but sigma was not constrained to be strictly positive.
diff --git a/docs/reference/model-method-diagnose.html b/docs/reference/model-method-diagnose.html
index e4120b50..9a09d308 100644
--- a/docs/reference/model-method-diagnose.html
+++ b/docs/reference/model-method-diagnose.html
@@ -165,10 +165,10 @@ Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.
A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -245,11 +245,11 @@
Examples
# retrieve the gradients
test$gradients()
-#> param_idx value model finite_diff error
-#> 1 0 0.466346 3.5478 3.5478 1.64210e-08
-#> 2 1 1.496850 -35.5646 -35.5646 6.49428e-09
-#> 3 2 1.385000 -27.1027 -27.1027 -1.36431e-08
-#> 4 3 -0.467569 14.0233 14.0233 1.05492e-08
+#> param_idx value model finite_diff error
+#> 1 0 -1.0700800 30.35590 30.35590 -8.78525e-09
+#> 2 1 0.5670250 -22.14500 -22.14500 9.99824e-09
+#> 3 2 0.0466329 -10.07860 -10.07860 1.68498e-08
+#> 4 3 1.1724800 -5.09201 -5.09201 2.79421e-10
# }
diff --git a/docs/reference/model-method-format.html b/docs/reference/model-method-format.html
index b0a71308..3e6ef100 100644
--- a/docs/reference/model-method-format.html
+++ b/docs/reference/model-method-format.html
@@ -212,7 +212,7 @@ Examples
# overwrite the original file instead of just printing it
mod$format(canonicalize = list("deprecations"), overwrite_file = TRUE)
-#> Old version of the model stored to /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model_1fc88c86300d78667dd3e476a636c279.stan.bak-20231108103429.
+#> Old version of the model stored to /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/model_1fc88c86300d78667dd3e476a636c279.stan.bak-20231213100754.
mod$compile()
diff --git a/docs/reference/model-method-laplace-1.png b/docs/reference/model-method-laplace-1.png
index 01b1f50d..aaf21c13 100644
Binary files a/docs/reference/model-method-laplace-1.png and b/docs/reference/model-method-laplace-1.png differ
diff --git a/docs/reference/model-method-laplace.html b/docs/reference/model-method-laplace.html
index 6a0d5658..20880c2f 100644
--- a/docs/reference/model-method-laplace.html
+++ b/docs/reference/model-method-laplace.html
@@ -1,5 +1,5 @@
-Run Stan's laplace algorithm — model-method-laplace • cmdstanr Run Stan's Laplace algorithm — model-method-laplace • cmdstanr
- Run Stan's laplace algorithm
+ Run Stan's Laplace algorithm
Source: R/model.R
model-method-laplace.Rd
@@ -192,10 +192,10 @@ Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.
A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -332,9 +332,9 @@
Examples
stan_data <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))
fit_mode <- mod$optimize(data = stan_data, jacobian = TRUE)
-#> Initial log joint probability = -6.78622
+#> Initial log joint probability = -8.0994
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 4 -6.74802 0.000186334 5.91326e-07 1 1 7
+#> 5 -6.74802 0.000245062 1.61833e-06 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -357,15 +357,15 @@ Examples
#> # A tibble: 3 × 7
#> variable mean median sd mad q5 q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.24 -6.98 0.677 0.309 -8.74 -6.75
-#> 2 lp_approx__ -0.505 -0.230 0.686 0.308 -1.92 -0.00197
-#> 3 theta 0.269 0.254 0.123 0.125 0.0982 0.500
+#> 1 lp__ -7.25 -6.97 0.724 0.305 -8.72 -6.75
+#> 2 lp_approx__ -0.516 -0.225 0.747 0.306 -1.93 -0.00324
+#> 3 theta 0.267 0.250 0.124 0.120 0.0954 0.499
# if mode isn't specified optimize is run internally first
fit_laplace <- mod$laplace(data = stan_data)
-#> Initial log joint probability = -6.7528
+#> Initial log joint probability = -15.1067
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 3 -6.74802 0.000769281 2.15262e-05 1 1 6
+#> 5 -6.74802 0.00203152 6.06231e-05 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -387,9 +387,9 @@ Examples
#> # A tibble: 3 × 7
#> variable mean median sd mad q5 q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.26 -6.95 0.755 0.289 -8.76 -6.75
-#> 2 lp_approx__ -0.518 -0.203 0.755 0.285 -2.15 -0.00170
-#> 3 theta 0.267 0.250 0.125 0.115 0.0947 0.505
+#> 1 lp__ -7.28 -6.98 0.782 0.316 -8.84 -6.75
+#> 2 lp_approx__ -0.531 -0.231 0.756 0.320 -1.96 -0.00280
+#> 3 theta 0.273 0.251 0.128 0.123 0.0984 0.510
# plot approximate posterior
bayesplot::mcmc_hist(fit_laplace$draws("theta"))
diff --git a/docs/reference/model-method-optimize.html b/docs/reference/model-method-optimize.html
index f88b0551..78b2a042 100644
--- a/docs/reference/model-method-optimize.html
+++ b/docs/reference/model-method-optimize.html
@@ -194,10 +194,10 @@ Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -531,12 +531,12 @@
Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -11.84
+#> Initial log joint probability = -7.17234
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 5 -6.74802 0.00117606 2.01092e-05 1 1 8
+#> 5 -6.74802 0.000196979 1.66919e-07 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -569,9 +569,9 @@ Examples
#> # A tibble: 3 × 7
#> variable mean median sd mad q5 q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.23 -6.96 0.703 0.291 -8.51 -6.75
-#> 2 lp_approx__ -0.490 -0.218 0.693 0.294 -1.85 -0.00308
-#> 3 theta 0.268 0.247 0.122 0.119 0.0992 0.495
+#> 1 lp__ -7.26 -6.98 0.759 0.313 -8.67 -6.75
+#> 2 lp_approx__ -0.521 -0.232 0.759 0.318 -2.07 -0.00238
+#> 3 theta 0.266 0.248 0.125 0.123 0.0975 0.496
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -580,8 +580,8 @@ Examples
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
-#> Gradient evaluation took 8e-06 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
+#> Gradient evaluation took 6e-06 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
@@ -640,6 +640,53 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -16.937625
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.488e-03 4.162e-05 1.000e+00 1.000e+00 126 -6.227e+00 -6.227e+00
+#> Path [1] :Best Iter: [2] ELBO (-6.211714) evaluations: (126)
+#> Path [2] :Initial log joint density = -6.798413
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 3.762e-03 4.627e-05 9.519e-01 9.519e-01 76 -6.195e+00 -6.195e+00
+#> Path [2] :Best Iter: [3] ELBO (-6.194747) evaluations: (76)
+#> Path [3] :Initial log joint density = -7.054440
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 2.741e-03 5.225e-05 1.000e+00 1.000e+00 101 -6.187e+00 -6.187e+00
+#> Path [3] :Best Iter: [4] ELBO (-6.186757) evaluations: (101)
+#> Path [4] :Initial log joint density = -7.467690
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.049e-04 4.080e-07 1.000e+00 1.000e+00 126 -6.224e+00 -6.224e+00
+#> Path [4] :Best Iter: [2] ELBO (-6.213804) evaluations: (126)
+#> Path [5] :Initial log joint density = -14.817294
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.041e-03 5.999e-05 1.000e+00 1.000e+00 126 -6.271e+00 -6.271e+00
+#> Path [5] :Best Iter: [3] ELBO (-6.188344) evaluations: (126)
+#> Path [6] :Initial log joint density = -7.375208
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 5.530e-04 1.038e-06 1.000e+00 1.000e+00 126 -6.229e+00 -6.229e+00
+#> Path [6] :Best Iter: [2] ELBO (-6.203592) evaluations: (126)
+#> Path [7] :Initial log joint density = -6.748023
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 2 -6.748e+00 9.521e-04 6.579e-07 1.000e+00 1.000e+00 51 -6.231e+00 -6.231e+00
+#> Path [7] :Best Iter: [2] ELBO (-6.231405) evaluations: (51)
+#> Path [8] :Initial log joint density = -11.312958
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.017e-03 1.576e-05 1.000e+00 1.000e+00 126 -6.304e+00 -6.304e+00
+#> Path [8] :Best Iter: [3] ELBO (-6.185214) evaluations: (126)
+#> Path [9] :Initial log joint density = -6.881067
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 9.878e-04 9.538e-06 1.000e+00 1.000e+00 101 -6.173e+00 -6.173e+00
+#> Path [9] :Best Iter: [4] ELBO (-6.173184) evaluations: (101)
+#> Path [10] :Initial log joint density = -7.173191
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.980e-04 1.685e-07 1.000e+00 1.000e+00 126 -6.176e+00 -6.176e+00
+#> Path [10] :Best Iter: [5] ELBO (-6.175625) evaluations: (126)
+#> Total log probability function evaluations:1235
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
diff --git a/docs/reference/model-method-pathfinder.html b/docs/reference/model-method-pathfinder.html
index f6423f16..e5caa27c 100644
--- a/docs/reference/model-method-pathfinder.html
+++ b/docs/reference/model-method-pathfinder.html
@@ -2,12 +2,15 @@
Run Stan's Pathfinder Variational Inference Algorithm — model-method-pathfinder • cmdstanr CmdStanModel object runs
Stan's Pathfinder algorithms. Pathfinder is a variational method for
approximately sampling from differentiable log densities. Starting from a
-random initialization, Pathfinder locates normal approximations to the
-target density along a quasi-Newton optimization path, with local
-covariance estimated using the negative inverse Hessian estimates produced
-by the L-BFGS optimizer. Pathfinder returns draws from the Gaussian
-approximation with the lowest estimated Kullback-Leibler (KL) divergence to
-the true posterior. See the
+random initialization, Pathfinder locates normal approximations
+to the target density along a quasi-Newton optimization path in
+the unconstrained space, with local covariance estimated using
+the negative inverse Hessian estimates produced by the LBFGS
+optimizer. Pathfinder selects the normal approximation with the
+lowest estimated Kullback-Leibler (KL) divergence to the true
+posterior. Finally Pathfinder draws from that normal
+approximation and returns the draws transformed to the
+constrained scale. See the
CmdStan User’s Guide
for more details.
Any argument left as NULL will default to the default value used by the
@@ -203,10 +209,10 @@
Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -310,8 +316,9 @@
Arguments
draws
(positive integer) Number of draws to return after performing
-pareto smooted importance sampling (PSIS). This must be smaller than
-single_path_draws * num_paths.
single_path_draws * num_paths (future versions of CmdStan will throw a
+warning).
num_paths
@@ -548,12 +555,12 @@ Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -8.53772
+#> Initial log joint probability = -11.5709
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 5 -6.74802 0.000313669 2.44314e-06 1 1 8
+#> 5 -6.74802 0.00107647 1.72219e-05 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -584,11 +591,11 @@ Examples
#> Finished in 0.1 seconds.
fit_laplace$summary()
#> # A tibble: 3 × 7
-#> variable mean median sd mad q5 q95
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.24 -6.97 0.690 0.299 -8.66 -6.75
-#> 2 lp_approx__ -0.502 -0.223 0.693 0.303 -1.94 -0.00213
-#> 3 theta 0.267 0.247 0.123 0.119 0.0995 0.501
+#> variable mean median sd mad q5 q95
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -7.23 -6.99 0.637 0.323 -8.54 -6.75
+#> 2 lp_approx__ -0.486 -0.238 0.644 0.323 -1.80 -0.00166
+#> 3 theta 0.268 0.252 0.121 0.126 0.104 0.492
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -597,8 +604,8 @@ Examples
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
-#> Gradient evaluation took 7e-06 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
+#> Gradient evaluation took 6e-06 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
@@ -657,6 +664,53 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -14.470660
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.026e-03 5.797e-05 1.000e+00 1.000e+00 126 -6.259e+00 -6.259e+00
+#> Path [1] :Best Iter: [4] ELBO (-6.211388) evaluations: (126)
+#> Path [2] :Initial log joint density = -9.064681
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 3.457e-04 2.927e-06 1.000e+00 1.000e+00 126 -6.253e+00 -6.253e+00
+#> Path [2] :Best Iter: [3] ELBO (-6.227371) evaluations: (126)
+#> Path [3] :Initial log joint density = -15.152638
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.028e-03 6.063e-05 1.000e+00 1.000e+00 126 -6.230e+00 -6.230e+00
+#> Path [3] :Best Iter: [4] ELBO (-6.211247) evaluations: (126)
+#> Path [4] :Initial log joint density = -10.220491
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 7.043e-04 8.900e-06 1.000e+00 1.000e+00 126 -6.194e+00 -6.194e+00
+#> Path [4] :Best Iter: [5] ELBO (-6.193620) evaluations: (126)
+#> Path [5] :Initial log joint density = -6.766009
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 2.724e-03 1.458e-04 1.000e+00 1.000e+00 76 -6.232e+00 -6.232e+00
+#> Path [5] :Best Iter: [3] ELBO (-6.232284) evaluations: (76)
+#> Path [6] :Initial log joint density = -7.304442
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 4.014e-04 5.879e-07 1.000e+00 1.000e+00 126 -6.247e+00 -6.247e+00
+#> Path [6] :Best Iter: [4] ELBO (-6.212233) evaluations: (126)
+#> Path [7] :Initial log joint density = -7.233791
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.804e-04 3.115e-07 1.000e+00 1.000e+00 126 -6.275e+00 -6.275e+00
+#> Path [7] :Best Iter: [4] ELBO (-6.122234) evaluations: (126)
+#> Path [8] :Initial log joint density = -11.942008
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.223e-03 2.161e-05 1.000e+00 1.000e+00 126 -6.232e+00 -6.232e+00
+#> Path [8] :Best Iter: [3] ELBO (-6.217220) evaluations: (126)
+#> Path [9] :Initial log joint density = -7.438064
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 9.817e-05 3.665e-07 1.000e+00 1.000e+00 126 -6.268e+00 -6.268e+00
+#> Path [9] :Best Iter: [3] ELBO (-6.213533) evaluations: (126)
+#> Path [10] :Initial log joint density = -8.992806
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 3.448e-04 2.904e-06 1.000e+00 1.000e+00 126 -6.247e+00 -6.247e+00
+#> Path [10] :Best Iter: [2] ELBO (-6.141839) evaluations: (126)
+#> Total log probability function evaluations:1360
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
diff --git a/docs/reference/model-method-sample.html b/docs/reference/model-method-sample.html
index 02207960..7925e4b4 100644
--- a/docs/reference/model-method-sample.html
+++ b/docs/reference/model-method-sample.html
@@ -210,10 +210,10 @@ Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -659,12 +659,12 @@
Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -7.01351
+#> Initial log joint probability = -11.0695
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 4 -6.74802 0.000544739 0.000133582 0.9055 0.9055 7
+#> 5 -6.74802 0.000954615 1.42918e-05 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -695,11 +695,11 @@ Examples
#> Finished in 0.1 seconds.
fit_laplace$summary()
#> # A tibble: 3 × 7
-#> variable mean median sd mad q5 q95
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.24 -6.98 0.692 0.314 -8.61 -6.75
-#> 2 lp_approx__ -0.500 -0.233 0.688 0.317 -1.89 -0.00172
-#> 3 theta 0.269 0.248 0.123 0.122 0.0989 0.503
+#> variable mean median sd mad q5 q95
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -7.23 -6.97 0.710 0.297 -8.57 -6.75
+#> 2 lp_approx__ -0.490 -0.220 0.696 0.300 -1.90 -0.00171
+#> 3 theta 0.270 0.252 0.123 0.120 0.102 0.495
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -708,8 +708,8 @@ Examples
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
-#> Gradient evaluation took 7e-06 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
+#> Gradient evaluation took 6e-06 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
@@ -768,6 +768,53 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -10.398597
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 7.601e-04 1.002e-05 1.000e+00 1.000e+00 126 -6.246e+00 -6.246e+00
+#> Path [1] :Best Iter: [4] ELBO (-6.213236) evaluations: (126)
+#> Path [2] :Initial log joint density = -7.343771
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 5.658e-03 1.740e-04 1.000e+00 1.000e+00 101 -6.270e+00 -6.270e+00
+#> Path [2] :Best Iter: [3] ELBO (-6.236555) evaluations: (101)
+#> Path [3] :Initial log joint density = -7.253954
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 4.780e-03 1.316e-04 1.000e+00 1.000e+00 101 -6.293e+00 -6.293e+00
+#> Path [3] :Best Iter: [3] ELBO (-6.210231) evaluations: (101)
+#> Path [4] :Initial log joint density = -6.884985
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 1.025e-03 1.015e-05 1.000e+00 1.000e+00 101 -6.254e+00 -6.254e+00
+#> Path [4] :Best Iter: [4] ELBO (-6.254382) evaluations: (101)
+#> Path [5] :Initial log joint density = -10.549385
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 8.063e-04 1.099e-05 1.000e+00 1.000e+00 126 -6.155e+00 -6.155e+00
+#> Path [5] :Best Iter: [5] ELBO (-6.154812) evaluations: (126)
+#> Path [6] :Initial log joint density = -11.021697
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 9.418e-04 1.400e-05 1.000e+00 1.000e+00 126 -6.261e+00 -6.261e+00
+#> Path [6] :Best Iter: [4] ELBO (-6.204645) evaluations: (126)
+#> Path [7] :Initial log joint density = -10.721189
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 8.573e-04 1.209e-05 1.000e+00 1.000e+00 126 -6.195e+00 -6.195e+00
+#> Path [7] :Best Iter: [5] ELBO (-6.194637) evaluations: (126)
+#> Path [8] :Initial log joint density = -11.729882
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.125e-03 1.854e-05 1.000e+00 1.000e+00 126 -6.195e+00 -6.195e+00
+#> Path [8] :Best Iter: [4] ELBO (-6.193964) evaluations: (126)
+#> Path [9] :Initial log joint density = -8.375820
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.924e-04 2.169e-06 1.000e+00 1.000e+00 126 -6.198e+00 -6.198e+00
+#> Path [9] :Best Iter: [2] ELBO (-6.188312) evaluations: (126)
+#> Path [10] :Initial log joint density = -7.041152
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 2.604e-03 4.795e-05 1.000e+00 1.000e+00 101 -6.233e+00 -6.233e+00
+#> Path [10] :Best Iter: [2] ELBO (-6.214834) evaluations: (101)
+#> Total log probability function evaluations:1310
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
diff --git a/docs/reference/model-method-sample_mpi.html b/docs/reference/model-method-sample_mpi.html
index 5dd06c42..6247b07a 100644
--- a/docs/reference/model-method-sample_mpi.html
+++ b/docs/reference/model-method-sample_mpi.html
@@ -1,7 +1,7 @@
Run Stan's MCMC algorithms with MPI — model-method-sample_mpi • cmdstanr
The $sample_mpi() method of a CmdStanModel object is
identical to the $sample() method but with support for
-MPI. The target audience for MPI are
+MPI (message passing interface). The target audience for MPI are
those with large computer clusters. For other users, the
$sample() method provides both parallelization of
chains and threading support for within-chain parallelization.
@@ -247,10 +247,10 @@ Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.
A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -467,8 +467,8 @@
See also
documentation and tutorials.
The Stan and CmdStan documentation:
Stan documentation: mc-stan.org/users/documentation
CmdStan User’s Guide: mc-stan.org/docs/cmdstan-guide
The Stan Math Library's MPI documentation
-(mc-stan.org/math/mpi) for more
+
The Stan Math Library's documentation
+(mc-stan.org/math) for more
details on MPI support in Stan.
Other CmdStanModel methods:
model-method-check_syntax,
diff --git a/docs/reference/model-method-variational.html b/docs/reference/model-method-variational.html
index 7aee6bce..ed9fcbc4 100644
--- a/docs/reference/model-method-variational.html
+++ b/docs/reference/model-method-variational.html
@@ -197,10 +197,10 @@
Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -534,12 +534,12 @@
Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -7.23249
+#> Initial log joint probability = -11.7793
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 4 -6.74802 0.00456563 0.000121975 1 1 7
+#> 5 -6.74802 0.00114772 1.92382e-05 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -572,9 +572,9 @@ Examples
#> # A tibble: 3 × 7
#> variable mean median sd mad q5 q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.25 -6.99 0.712 0.326 -8.66 -6.75
-#> 2 lp_approx__ -0.506 -0.244 0.715 0.329 -1.94 -0.00187
-#> 3 theta 0.269 0.250 0.124 0.126 0.101 0.500
+#> 1 lp__ -7.22 -6.96 0.669 0.294 -8.51 -6.75
+#> 2 lp_approx__ -0.470 -0.213 0.645 0.295 -1.81 -0.00204
+#> 3 theta 0.267 0.247 0.121 0.118 0.104 0.493
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -583,8 +583,8 @@ Examples
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
-#> Gradient evaluation took 7e-06 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
+#> Gradient evaluation took 5e-06 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
@@ -643,6 +643,54 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -7.711026
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.614e-04 8.188e-07 1.000e+00 1.000e+00 126 -6.206e+00 -6.206e+00
+#> Path [1] :Best Iter: [5] ELBO (-6.205511) evaluations: (126)
+#> Path [2] :Initial log joint density = -12.450513
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.452e-03 2.960e-05 1.000e+00 1.000e+00 126 -6.251e+00 -6.251e+00
+#> Path [2] :Best Iter: [4] ELBO (-6.229238) evaluations: (126)
+#> Path [3] :Initial log joint density = -7.647405
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.466e-04 7.009e-07 1.000e+00 1.000e+00 126 -6.193e+00 -6.193e+00
+#> Path [3] :Best Iter: [3] ELBO (-6.164986) evaluations: (126)
+#> Path [4] :Initial log joint density = -15.036045
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.036e-03 6.056e-05 1.000e+00 1.000e+00 126 -6.235e+00 -6.235e+00
+#> Path [4] :Best Iter: [2] ELBO (-6.225650) evaluations: (126)
+#> Path [5] :Initial log joint density = -6.760365
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 1.911e-03 8.518e-05 1.000e+00 1.000e+00 76 -6.247e+00 -6.247e+00
+#> Path [5] :Best Iter: [3] ELBO (-6.246721) evaluations: (76)
+#> Path [6] :Initial log joint density = -6.826898
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 5.423e-03 1.282e-04 9.360e-01 9.360e-01 76 -6.169e+00 -6.169e+00
+#> Path [6] :Best Iter: [3] ELBO (-6.168890) evaluations: (76)
+#> Path [7] :Initial log joint density = -8.134235
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.517e-04 1.691e-06 1.000e+00 1.000e+00 126 -6.243e+00 -6.243e+00
+#> Path [7] :Best Iter: [3] ELBO (-6.175493) evaluations: (126)
+#> Path [8] :Initial log joint density = -7.511494
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 9.446e-04 2.687e-06 1.000e+00 1.000e+00 126 -6.214e+00 -6.214e+00
+#> Path [8] :Best Iter: [2] ELBO (-6.197279) evaluations: (126)
+#> Path [9] :Initial log joint density = -11.516990
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.065e-03 1.693e-05 1.000e+00 1.000e+00 126 -6.244e+00 -6.244e+00
+#> Path [9] :Best Iter: [3] ELBO (-6.194623) evaluations: (126)
+#> Path [10] :Initial log joint density = -17.778190
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.013e-03 2.414e-05 1.000e+00 1.000e+00 126 -6.262e+00 -6.262e+00
+#> Path [10] :Best Iter: [4] ELBO (-6.170410) evaluations: (126)
+#> Total log probability function evaluations:1310
+#> Pareto k value (0.820024) is greater than 0.7. Importance resampling was not able to improve the approximation, which may indicate that the approximation itself is poor.
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
diff --git a/docs/reference/read_cmdstan_csv.html b/docs/reference/read_cmdstan_csv.html
index d5b0c50d..d6b8fb74 100644
--- a/docs/reference/read_cmdstan_csv.html
+++ b/docs/reference/read_cmdstan_csv.html
@@ -223,10 +223,10 @@ Examples
fit1 <- cmdstanr_example("logistic", method = "sample", save_warmup = TRUE)
csv_files <- fit1$output_files()
print(csv_files)
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-1-32a06f.csv"
-#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-2-32a06f.csv"
-#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-3-32a06f.csv"
-#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-4-32a06f.csv"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131008-1-642e8e.csv"
+#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131008-2-642e8e.csv"
+#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131008-3-642e8e.csv"
+#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131008-4-642e8e.csv"
# Creating fitting model objects
@@ -234,9 +234,9 @@ Examples
fit2 <- as_cmdstan_fit(csv_files)
fit2$print("beta")
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
-#> beta[1] -0.67 -0.66 0.25 0.24 -1.08 -0.28 1.00 4402 2852
-#> beta[2] -0.27 -0.27 0.22 0.22 -0.65 0.09 1.00 4396 2793
-#> beta[3] 0.68 0.67 0.27 0.26 0.27 1.12 1.00 4545 3039
+#> beta[1] -0.67 -0.66 0.25 0.25 -1.09 -0.26 1.00 4234 3123
+#> beta[2] -0.28 -0.27 0.23 0.23 -0.65 0.09 1.00 4067 3185
+#> beta[3] 0.69 0.67 0.27 0.27 0.26 1.14 1.00 4291 3208
# Using read_cmdstan_csv
#
@@ -248,7 +248,7 @@ Examples
#> ..$ stan_version_major : num 2
#> ..$ stan_version_minor : num 33
#> ..$ stan_version_patch : num 0
-#> ..$ start_datetime : chr "2023-11-08 17:35:02 UTC"
+#> ..$ start_datetime : chr "2023-12-13 17:08:27 UTC"
#> ..$ method : chr "sample"
#> ..$ save_warmup : num 1
#> ..$ thin : num 1
@@ -265,14 +265,14 @@ Examples
#> ..$ num_chains : num 1
#> ..$ id : num [1:4] 1 2 3 4
#> ..$ init : num [1:4] 2 2 2 2
-#> ..$ seed : num 1.63e+09
+#> ..$ seed : num 1.1e+09
#> ..$ refresh : num 100
#> ..$ sig_figs : num -1
-#> ..$ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081035-1-02259b.csv"
+#> ..$ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131008-1-0e2cb1.csv"
#> ..$ stanc_version : chr "stanc3 v2.33.1"
#> ..$ sampler_diagnostics : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ...
#> ..$ variables : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
-#> ..$ step_size_adaptation: num [1:4] 0.731 0.842 0.658 0.727
+#> ..$ step_size_adaptation: num [1:4] 0.761 0.758 0.859 0.765
#> ..$ model_name : chr "logistic_model"
#> ..$ adapt_engaged : num 1
#> ..$ adapt_delta : num 0.8
@@ -283,9 +283,9 @@ Examples
#> ..$ threads_per_chain : num 1
#> ..$ time :'data.frame': 4 obs. of 4 variables:
#> .. ..$ chain_id: num [1:4] 1 2 3 4
-#> .. ..$ warmup : num [1:4] 0.071 0.073 0.078 0.075
-#> .. ..$ sampling: num [1:4] 0.075 0.089 0.078 0.073
-#> .. ..$ total : num [1:4] 0.146 0.162 0.156 0.148
+#> .. ..$ warmup : num [1:4] 0.071 0.073 0.072 0.069
+#> .. ..$ sampling: num [1:4] 0.068 0.068 0.065 0.068
+#> .. ..$ total : num [1:4] 0.139 0.141 0.137 0.137
#> ..$ stan_variable_sizes :List of 4
#> .. ..$ lp__ : num 1
#> .. ..$ alpha : num 1
@@ -297,35 +297,35 @@ Examples
#> ..$ total : int NA
#> ..$ chains:'data.frame': 4 obs. of 4 variables:
#> .. ..$ chain_id: num [1:4] 1 2 3 4
-#> .. ..$ warmup : num [1:4] 0.071 0.073 0.078 0.075
-#> .. ..$ sampling: num [1:4] 0.075 0.089 0.078 0.073
-#> .. ..$ total : num [1:4] 0.146 0.162 0.156 0.148
+#> .. ..$ warmup : num [1:4] 0.071 0.073 0.072 0.069
+#> .. ..$ sampling: num [1:4] 0.068 0.068 0.065 0.068
+#> .. ..$ total : num [1:4] 0.139 0.141 0.137 0.137
#> $ inv_metric :List of 4
-#> ..$ 1: num [1:4] 0.0464 0.0532 0.0478 0.0741
-#> ..$ 2: num [1:4] 0.0374 0.0624 0.0482 0.0688
-#> ..$ 3: num [1:4] 0.042 0.0545 0.0473 0.0754
-#> ..$ 4: num [1:4] 0.0411 0.0563 0.0539 0.0609
+#> ..$ 1: num [1:4] 0.0469 0.0641 0.0523 0.0688
+#> ..$ 2: num [1:4] 0.0438 0.0555 0.0497 0.0685
+#> ..$ 3: num [1:4] 0.0457 0.0612 0.0451 0.0706
+#> ..$ 4: num [1:4] 0.0436 0.0627 0.0506 0.0832
#> $ step_size :List of 4
-#> ..$ 1: num 0.731
-#> ..$ 2: num 0.842
-#> ..$ 3: num 0.658
-#> ..$ 4: num 0.727
-#> $ warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -78.1 -78.1 -78.1 -64.4 -66.7 ...
+#> ..$ 1: num 0.761
+#> ..$ 2: num 0.758
+#> ..$ 3: num 0.859
+#> ..$ 4: num 0.765
+#> $ warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -72 -72 -72 -71.3 -68.2 ...
#> ..- attr(*, "dimnames")=List of 3
#> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
#> .. ..$ chain : chr [1:4] "1" "2" "3" "4"
#> .. ..$ variable : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
-#> $ post_warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -68.5 -66.2 -68 -68.3 -67.2 ...
+#> $ post_warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -64.3 -64.3 -65.7 -67.2 -66 ...
#> ..- attr(*, "dimnames")=List of 3
#> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
#> .. ..$ chain : chr [1:4] "1" "2" "3" "4"
#> .. ..$ variable : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
-#> $ warmup_sampler_diagnostics : 'draws_array' num [1:1000, 1:4, 1:6] 1 0 0 1 0.515 ...
+#> $ warmup_sampler_diagnostics : 'draws_array' num [1:1000, 1:4, 1:6] 1 0 0 0.597 1 ...
#> ..- attr(*, "dimnames")=List of 3
#> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
#> .. ..$ chain : chr [1:4] "1" "2" "3" "4"
#> .. ..$ variable : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ...
-#> $ post_warmup_sampler_diagnostics: 'draws_array' num [1:1000, 1:4, 1:6] 0.856 1 0.856 0.984 1 ...
+#> $ post_warmup_sampler_diagnostics: 'draws_array' num [1:1000, 1:4, 1:6] 0.971 0.97 0.742 0.882 0.87 ...
#> ..- attr(*, "dimnames")=List of 3
#> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
#> .. ..$ chain : chr [1:4] "1" "2" "3" "4"
diff --git a/docs/reference/write_stan_file.html b/docs/reference/write_stan_file.html
index 10ffeb6e..e1459132 100644
--- a/docs/reference/write_stan_file.html
+++ b/docs/reference/write_stan_file.html
@@ -188,7 +188,7 @@ Examples
f <- write_stan_file(stan_program)
print(f)
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model_7f12fc190dd23b0e462f7d73040dd97e.stan"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/model_7f12fc190dd23b0e462f7d73040dd97e.stan"
lines <- readLines(f)
print(lines)
diff --git a/docs/reference/write_stan_json.html b/docs/reference/write_stan_json.html
index d09ca22c..b7b128ef 100644
--- a/docs/reference/write_stan_json.html
+++ b/docs/reference/write_stan_json.html
@@ -167,13 +167,13 @@ Examples
#> "N": 5,
#> "K": 2,
#> "x": [
-#> [0.539316345307751, 1.20165111819522],
-#> [0.027826781785157, -0.219847554976374],
-#> [-0.171509078741447, 1.08053600860179],
-#> [1.01158266854045, 0.0611935712707024],
-#> [-0.426238537104135, 0.270940591850318]
+#> [0.281077678546936, -1.18908686575029],
+#> [0.858726751146771, 0.46461805175525],
+#> [-0.700205759803986, -0.115709696625946],
+#> [1.45853659080418, 0.995745564145492],
+#> [-0.671485199795285, -0.298752493529616]
#> ],
-#> "y": [10, 8, 13, 10, 12],
+#> "y": [12, 6, 11, 9, 11],
#> "z": [1, 0]
#> }
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index 6866e056..74c96150 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -3,6 +3,9 @@
https://mc-stan.org/cmdstanr/404.html
+
+ https://mc-stan.org/cmdstanr/CONTRIBUTING.html
+
https://mc-stan.org/cmdstanr/LICENSE-text.html
mod_pedantic <- cmdstan_model(stan_file_pedantic, pedantic = TRUE) -Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d863015ed4.stan', line 11, column 14: A +Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-74281eb75594.stan', line 11, column 14: A poisson distribution is given parameter lambda as a rate parameter (argument 1), but lambda was not constrained to be strictly positive. Warning: The parameter lambda has no priors. This means either no prior is @@ -288,7 +288,7 @@argument to thePedantic checkpedantic
$check_syntax()method.mod_pedantic$check_syntax(pedantic = TRUE) -Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A +Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A poisson distribution is given parameter lambda as a rate parameter (argument 1), but lambda was not constrained to be strictly positive. Warning: The parameter lambda has no priors. This means either no prior is @@ -305,7 +305,7 @@Pedantic check mod_pedantic <- cmdstan_model(stan_file_pedantic, compile = FALSE) mod_pedantic$check_syntax(pedantic = TRUE) -Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A +Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A poisson distribution is given parameter lambda as a rate parameter (argument 1), but lambda was not constrained to be strictly positive. Warning: The parameter lambda has no priors. This means either no prior is @@ -495,10 +495,10 @@
Default temporary files
-fit$output_files()+[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-1-36f995.csv" -[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-2-36f995.csv" -[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-3-36f995.csv" -[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-4-36f995.csv"[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-1-37e810.csv" +[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-2-37e810.csv" +[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-3-37e810.csv" +[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-4-37e810.csv"These files will be lost if you end your R session or if you remove the
@@ -510,8 +510,8 @@fitobject and force (or wait for) garbage collection.Default temporary filesrm(fit) gc()
+Ncells 1260600 67.4 2436292 130.2 NA 2436292 130.2 +Vcells 2212085 16.9 8388608 64.0 32768 3459642 26.4used (Mb) gc trigger (Mb) limit (Mb) max used (Mb) -Ncells 1249060 66.8 2424964 129.6 NA 2424964 129.6 -Vcells 2192048 16.8 8388608 64.0 32768 3407356 26.0
file.exists(files)[1] FALSE FALSE FALSE FALSELazy CSV readingLazy CSV reading
After we call a method that requires the draws then if we reexamine
@@ -623,14 +623,14 @@
Lazy CSV readingLazy CSV readingLazy CSV reading
For models with many parameters, transformed parameters, or generated
@@ -688,7 +688,7 @@
read_cmdstan_csv()read_cmdstan_csv()read_cmdstan_csv()read_cmdstan_csv()Saving and
fit <- mod$sample(data = data_list, save_latent_dynamics = TRUE)
For models with many parameters, transformed parameters, or generated @@ -688,7 +688,7 @@
read_cmdstan_csv()read_cmdstan_csv()read_cmdstan_csv()read_cmdstan_csv()Saving and
fit <- mod$sample(data = data_list, save_latent_dynamics = TRUE)
fit$latent_dynamics_files()[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-1-460fca.csv"
-[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-2-460fca.csv"
-[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-3-460fca.csv"
-[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-4-460fca.csv"[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-diagnostic-202312131009-1-425883.csv"
+[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-diagnostic-202312131009-2-425883.csv"
+[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-diagnostic-202312131009-3-425883.csv"
+[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-diagnostic-202312131009-4-425883.csv"
# read one of the files in
x <- utils::read.csv(fit$latent_dynamics_files()[1], comment.char = "#")
head(x) lp__ accept_stat__ stepsize__ treedepth__ n_leapfrog__ divergent__
-1 -6.84314 0.979774 0.874237 1 3 0
-2 -7.10483 0.971803 0.874237 2 3 0
-3 -6.85492 1.000000 0.874237 1 1 0
-4 -6.81455 0.951680 0.874237 2 3 0
-5 -6.90994 0.917527 0.874237 2 3 0
-6 -6.75277 1.000000 0.874237 1 3 0
- energy__ theta p_theta g_theta
-1 6.84887 -0.814342 0.139388 0.683591
-2 7.10784 -0.558031 -0.101020 1.368040
-3 7.03534 -0.797632 0.782091 0.726389
-4 7.12956 -0.860038 1.033420 0.567976
-5 7.27609 -0.730035 -1.114150 0.902245
-6 6.87890 -1.033970 -0.653912 0.147785lp__ is also provided via
fit$draws(), and the columns accept_stat__,
stepsize__, treedepth__,
@@ -818,13 +818,13 @@ theta p_theta g_theta
-1 -0.814342 0.139388 0.683591
-2 -0.558031 -0.101020 1.368040
-3 -0.797632 0.782091 0.726389
-4 -0.860038 1.033420 0.567976
-5 -0.730035 -1.114150 0.902245
-6 -1.033970 -0.653912 0.147785 theta p_theta g_theta
+1 0.3939970 -2.155610 4.166930
+2 -1.0295100 -2.933880 0.158161
+3 0.0530292 0.702591 3.159050
+4 -0.2148440 1.637290 2.357940
+5 -1.3633100 1.875250 -0.555575
+6 -1.3633100 -2.143570 -0.555575theta and the three
columns above correspond to theta in the
unconstrained space (theta on the constrained
@@ -872,16 +872,16 @@ Running make \
- /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc \
+ /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467 \
"STANCFLAGS += --name='bernoulli_model'"
--- Translating Stan model to C++ code ---
-bin/stanc --name='bernoulli_model' --o=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.hpp /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.stan
+bin/stanc --name='bernoulli_model' --o=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.hpp /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/model-742871ce8467.stan
--- Compiling, linking C++ code ---
-clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -c -include-pch stan/src/stan/model/model_header_13_0.hpp.gch -x c++ -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.hpp
-clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.o src/cmdstan/main.o -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc
-rm -f /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.o
fit <- mod$sample(
data = data_list,
@@ -891,11 +891,11 @@ Troubleshooting and debugging)
Running MCMC with 1 chain...
-Running ./bernoulli 'id=1' random 'seed=2046085541' data \
- 'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/standata-131d874013405.json' \
+Running ./bernoulli 'id=1' random 'seed=90958316' data \
+ 'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/standata-742874baeb02.json' \
output \
- 'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081036-1-2ef601.csv' \
- 'profile_file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-profile-202311081036-1-0c4891.csv' \
+ 'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-202312131009-1-4db120.csv' \
+ 'profile_file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpXOnqh8/bernoulli-profile-202312131009-1-1c1f83.csv' \
'method=sample' 'num_samples=100' 'num_warmup=100' 'save_warmup=0' \
'algorithm=hmc' 'engine=nuts' adapt 'engaged=1'
Chain 1 method = sample (Default)
@@ -925,19 +925,19 @@ Troubleshooting and debuggingSampler diagnostic warnings a
suffers from divergences.
fit_with_warning <- cmdstanr_example("schools")
-Warning: 124 of 4000 (3.0%) transitions ended with a divergence.
-See https://mc-stan.org/misc/warnings for details.
-Warning: 1 of 4 chains had an E-BFMI less than 0.2.
+Chain 4 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+Chain 4 Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmpl3ZPjU/model-76ac71f3c1fe.stan', line 14, column 2 to column 41)
+Chain 4 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+Chain 4 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+Chain 4
+Warning: 149 of 4000 (4.0%) transitions ended with a divergence.
See https://mc-stan.org/misc/warnings for details.
After fitting there is a warning about divergences. We can also
regenerate this warning message later using
fit$diagnostic_summary().
-
+
diagnostics <- fit_with_warning$diagnostic_summary()
-Warning: 124 of 4000 (3.0%) transitions ended with a divergence.
-See https://mc-stan.org/misc/warnings for details.
-Warning: 1 of 4 chains had an E-BFMI less than 0.2.
+Warning: 149 of 4000 (4.0%) transitions ended with a divergence.
See https://mc-stan.org/misc/warnings for details.
-
+
print(diagnostics)
$num_divergent
-[1] 3 12 97 12
+[1] 11 54 32 52
$num_max_treedepth
[1] 0 0 0 0
$ebfmi
-[1] 0.20 0.29 0.29 0.43
-
+[1] 0.40 0.36 0.37 0.30
+
# number of divergences reported in warning is the sum of the per chain values
sum(diagnostics$num_divergent)
-[1] 124
+[1] 149
CmdStan’s diagnose utility
@@ -543,7 +544,7 @@ Create a stanfit object
+
stanfit <- rstan::read_stan_csv(fit$output_files())
@@ -561,26 +562,26 @@ Optimization$optimize().
-
+
fit_mle <- mod$optimize(data = data_list, seed = 123)
Initial log joint probability = -9.51104
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
6 -5.00402 0.000103557 2.55661e-07 1 1 9
Optimization terminated normally:
Convergence detected: relative gradient magnitude is below tolerance
-Finished in 0.1 seconds.
-
+Finished in 0.2 seconds.
+
fit_mle$print() # includes lp__ (log prob calculated by Stan program)
variable estimate
lp__ -5.00
theta 0.20
-
+
fit_mle$mle("theta")
theta
0.2
Here’s a plot comparing the penalized MLE to the posterior
distribution of theta.
-
+
@@ -591,7 +592,7 @@ OptimizationMaximum
Likelihood Estimation section of the CmdStan User’s Guide for more
details.
-
+
fit_map <- mod$optimize(
data = data_list,
jacobian = TRUE,
@@ -620,7 +621,7 @@ Laplace Approximationmode
argument. If mode is omitted then
optimization will be run internally before taking draws from the normal
approximation.
-
+
fit_laplace <- mod$laplace(
mode = fit_map,
draws = 4000,
@@ -636,11 +637,11 @@ Laplace Approximation
-
+
fit_laplace$print("theta")
variable mean median sd mad q5 q95
theta 0.27 0.25 0.12 0.12 0.10 0.50
-
+
mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025)
@@ -651,7 +652,7 @@ Variational (ADVI)$variational()
method. For details on the ADVI algorithm see the CmdStan
User’s Guide.
-
+
fit_vb <- mod$variational(
data = data_list,
seed = 123,
@@ -662,8 +663,8 @@ Variational (ADVI)Variational (ADVI)
+
fit_vb$print("theta")
variable mean median sd mad q5 q95
theta 0.27 0.25 0.12 0.12 0.10 0.49
-
+
mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025)
@@ -696,7 +697,7 @@ Variational (Pathfinder)CmdStan
User’s Guide. Pathfinder is run using the $pathfinder()
method.
-
+
fit_pf <- mod$pathfinder(
data = data_list,
seed = 123,
@@ -720,7 +721,7 @@ Variational (Pathfinder)
-
+
fit_pf$print("theta")
variable mean median sd mad q5 q95
theta 0.25 0.24 0.12 0.12 0.08 0.47
@@ -728,22 +729,22 @@ Variational (Pathfinder)
-
+
mcmc_hist(fit_pf$draws("theta"), binwidth = 0.025) +
ggplot2::labs(subtitle = "Approximate posterior from pathfinder") +
ggplot2::xlim(0, 1)
-
+
mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025) +
ggplot2::labs(subtitle = "Approximate posterior from variational") +
ggplot2::xlim(0, 1)
-
+
mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025) +
ggplot2::labs(subtitle = "Approximate posterior from Laplace") +
ggplot2::xlim(0, 1)
-
+
mcmc_hist(fit$draws("theta"), binwidth = 0.025) +
ggplot2::labs(subtitle = "Posterior from MCMC") +
ggplot2::xlim(0, 1)
@@ -767,7 +768,7 @@ Saving fitted model objects
+
fit$save_object(file = "fit.RDS")
# can be read back in using readRDS
@@ -779,7 +780,7 @@ Saving fitted model objects$save_object()
and replace saveRDS with the much faster
qsave() function from the qs package.
-
+
# Load CmdStan output files into the fitted model object.
fit$draws() # Load posterior draws into the object.
try(fit$sampler_diagnostics(), silent = TRUE) # Load sampler diagnostics.
@@ -795,7 +796,7 @@ Saving fitted model objects
+
# Load posterior draws into the fitted model object and omit other output.
fit$draws()
@@ -828,8 +829,8 @@ Advantages of RStanDeveloping
+CRAN. (Note: As of 2023, this can mostly be achieved with CmdStanR as
+well. See Developing
using CmdStanR.)
Avoids use of R6 classes, which may result in more familiar
syntax for many R users.
Summary statistics
fit <- cmdstanr::cmdstanr_example("schools", method = "sample")
-Warning: 191 of 4000 (5.0%) transitions ended with a divergence.
+Warning: 411 of 4000 (10.0%) transitions ended with a divergence.
See https://mc-stan.org/misc/warnings for details.
-
+Warning: 1 of 4 chains had an E-BFMI less than 0.2.
+See https://mc-stan.org/misc/warnings for details.
+
fit$summary()
- variable mean median sd mad q5 q95
-1 lp__ -57.917364 -58.126700 5.227350 5.348035 -66.0514450 -47.99920
-2 mu 6.421661 6.560445 3.994261 3.728717 -0.1207198 13.16044
-3 tau 5.211890 4.447485 3.457118 3.180740 1.0590100 12.09968
-4 theta[1] 9.159825 8.066620 6.731183 5.516199 0.1858543 21.43202
-5 theta[2] 6.750832 6.740980 5.320603 4.835678 -1.7502315 15.66325
-6 theta[3] 5.564623 5.963075 6.485297 5.310451 -5.2589090 15.65180
-7 theta[4] 6.506969 6.552735 5.753473 4.933870 -2.8831600 15.96813
-8 theta[5] 4.602639 4.969540 5.596007 4.792030 -5.1313190 13.08000
-9 theta[6] 5.451744 5.604770 5.781909 4.976021 -4.4380795 14.88214
-10 theta[7] 9.105948 8.421645 6.077003 5.363817 0.4464001 20.13298
-11 theta[8] 6.904986 6.666570 6.591653 5.244594 -3.4154830 18.00026
- rhat ess_bulk ess_tail
-1 1.037320 87.01265 18.13542
-2 1.019548 741.86144 1129.22230
-3 1.041517 74.40670 16.64168
-4 1.012043 615.84853 1757.06290
-5 1.008696 1279.50483 2218.60618
-6 1.002604 1345.71326 1786.80529
-7 1.010044 1291.76655 1900.28317
-8 1.008882 1209.92612 1848.18547
-9 1.034876 1464.53969 1882.25434
-10 1.008780 554.66658 1685.21958
-11 1.034006 1102.79917 1784.94313
+ variable mean median sd mad q5 q95
+1 lp__ -55.581147 -56.536200 7.135791 7.809299 -66.108490 -44.17410
+2 mu 6.053657 5.516030 3.777403 3.597633 -0.119312 12.18247
+3 tau 4.456447 3.456865 3.641807 3.645828 0.778481 11.91315
+4 theta[1] 8.436941 7.287125 6.350694 4.628344 0.249281 20.38983
+5 theta[2] 6.522999 5.993620 5.308671 4.494072 -1.760367 15.64359
+6 theta[3] 5.147676 4.349235 6.014958 5.082679 -5.803791 14.17618
+7 theta[4] 6.189408 5.541310 5.578086 4.501730 -2.557880 15.63053
+8 theta[5] 4.669689 4.952550 5.338198 4.271882 -5.029484 12.96141
+9 theta[6] 5.123046 4.853110 5.623562 4.579796 -4.797084 13.70336
+10 theta[7] 8.283195 7.324155 5.703079 4.912514 1.112880 18.94623
+11 theta[8] 6.499894 5.594240 6.114406 4.839688 -2.747390 16.75831
+ rhat ess_bulk ess_tail
+1 1.348190 9.585696 NA
+2 1.094243 190.870204 910.104440
+3 1.305410 10.562073 4.833756
+4 1.037209 205.819798 1097.512010
+5 1.234963 615.265251 1158.714996
+6 1.270209 429.358164 1118.125260
+7 1.060915 288.566093 1331.398737
+8 1.265317 356.169676 1352.544578
+9 1.281493 482.412281 1311.140711
+10 1.041368 75.113061 938.699910
+11 1.069623 436.937773 1068.232198
By default all variables are summaries with the follow functions:
-
+
posterior::default_summary_measures()
[1] "mean" "median" "sd" "mad" "quantile2"
To change the variables summarized, we use the variables argument
-
+
fit$summary(variables = c("mu", "tau"))
- variable mean median sd mad q5 q95 rhat
-1 mu 6.421661 6.560445 3.994261 3.728717 -0.1207198 13.16044 1.019548
-2 tau 5.211890 4.447485 3.457118 3.180740 1.0590100 12.09968 1.041517
- ess_bulk ess_tail
-1 741.8614 1129.22230
-2 74.4067 16.64168
+ variable mean median sd mad q5 q95 rhat
+1 mu 6.053657 5.516030 3.777403 3.597633 -0.119312 12.18247 1.094243
+2 tau 4.456447 3.456865 3.641807 3.645828 0.778481 11.91315 1.305410
+ ess_bulk ess_tail
+1 190.87020 910.104440
+2 10.56207 4.833756
We can additionally change which functions are used
-
+
fit$summary(variables = c("mu", "tau"), mean, sd)
variable mean sd
-1 mu 6.421661 3.994261
-2 tau 5.211890 3.457118
+1 mu 6.053657 3.777403
+2 tau 4.456447 3.641807
To summarize all variables with non-default functions, it is
necessary to set explicitly set the variables argument, either to
NULL or the full vector of variable names.
-
+
fit$metadata()$model_params
[1] "lp__" "mu" "tau" "theta[1]" "theta[2]" "theta[3]"
[7] "theta[4]" "theta[5]" "theta[6]" "theta[7]" "theta[8]"
-
+
fit$summary(variables = NULL, "mean", "median")
variable mean median
-1 lp__ -57.917364 -58.126700
-2 mu 6.421661 6.560445
-3 tau 5.211890 4.447485
-4 theta[1] 9.159825 8.066620
-5 theta[2] 6.750832 6.740980
-6 theta[3] 5.564623 5.963075
-7 theta[4] 6.506969 6.552735
-8 theta[5] 4.602639 4.969540
-9 theta[6] 5.451744 5.604770
-10 theta[7] 9.105948 8.421645
-11 theta[8] 6.904986 6.666570
+1 lp__ -55.581147 -56.536200
+2 mu 6.053657 5.516030
+3 tau 4.456447 3.456865
+4 theta[1] 8.436941 7.287125
+5 theta[2] 6.522999 5.993620
+6 theta[3] 5.147676 4.349235
+7 theta[4] 6.189408 5.541310
+8 theta[5] 4.669689 4.952550
+9 theta[6] 5.123046 4.853110
+10 theta[7] 8.283195 7.324155
+11 theta[8] 6.499894 5.594240
Summary functions can be specified by character string, function, or
using a formula (or anything else supported by
rlang::as_function()). If these arguments are named, those
names will be used in the tibble output. If the summary results are
named they will take precedence.
-
+
my_sd <- function(x) c(My_SD = sd(x))
fit$summary(
c("mu", "tau"),
@@ -230,45 +232,45 @@ Summary statistics~quantile(.x, probs = c(0.1, 0.9)),
Minimum = function(x) min(x)
)
- variable MEAN median My_SD 10% 90% Minimum
-1 mu 6.421661 6.560445 3.994261 1.488318 11.54665 -15.442700
-2 tau 5.211890 4.447485 3.457118 1.488050 10.02499 0.822982
+ variable MEAN median My_SD 10% 90% Minimum
+1 mu 6.053657 5.516030 3.777403 1.565533 10.863700 -8.554010
+2 tau 4.456447 3.456865 3.641807 0.778481 9.666494 0.774844
Arguments to all summary functions can also be specified with
.args.
-
+
- variable 2.5% 5% 95% 97.5%
-1 mu -1.441798 -0.1207198 13.16044 14.63687
-2 tau 0.822982 1.0590100 12.09968 13.78430
+ variable 2.5% 5% 95% 97.5%
+1 mu -1.614829 -0.119312 12.18247 13.54298
+2 tau 0.778481 0.778481 11.91315 13.43694
The summary functions are applied to the array of sample values, with
dimension iter_samplingxchains.
-
+
fit$summary(variables = NULL, dim, colMeans)
variable dim.1 dim.2 1 2 3 4
-1 lp__ 1000 4 -59.408601 -58.175641 -58.144473 -55.940741
-2 mu 1000 4 6.689529 6.327474 6.548562 6.121077
-3 tau 1000 4 6.015816 5.271415 5.222288 4.338039
-4 theta[1] 1000 4 10.309827 9.010128 9.089769 8.229576
-5 theta[2] 1000 4 6.940841 6.642238 6.917915 6.502333
-6 theta[3] 1000 4 5.378242 5.517793 5.881470 5.480988
-7 theta[4] 1000 4 6.732677 6.458448 6.658988 6.177763
-8 theta[5] 1000 4 4.460429 4.316417 4.822090 4.811620
-9 theta[6] 1000 4 5.567601 5.449356 5.743810 5.046207
-10 theta[7] 1000 4 9.872239 9.168575 9.143428 8.239549
-11 theta[8] 1000 4 6.985384 6.844136 7.146715 6.643711
+1 lp__ 1000 4 -56.698590 -58.592226 -56.911658 -50.122112
+2 mu 1000 4 6.836467 6.338743 6.214918 4.824501
+3 tau 1000 4 4.751132 5.540196 4.617587 2.916872
+4 theta[1] 1000 4 9.139180 9.470688 8.631066 6.506831
+5 theta[2] 1000 4 7.088238 6.756565 6.691301 5.555891
+6 theta[3] 1000 4 6.087235 5.430155 5.208329 3.864985
+7 theta[4] 1000 4 6.963234 6.883517 6.322756 4.588127
+8 theta[5] 1000 4 5.312623 4.530775 4.623091 4.212267
+9 theta[6] 1000 4 5.778286 4.999767 5.497782 4.216348
+10 theta[7] 1000 4 9.354323 9.367796 8.483796 5.926863
+11 theta[8] 1000 4 7.269928 7.165762 6.670007 4.893880
For this reason users may have unexpected results if they use
stats::var() directly, as it will return a covariance
matrix. An alternative is the distributional::variance()
function, which can also be accessed via
posterior::variance().
-
+
variable posterior::variance ~var(as.vector(.x))
-1 mu 15.95412 15.95412
-2 tau 11.95166 11.95166
+1 mu 14.26877 14.26877
+2 tau 13.26276 13.26276
Summary functions need not be numeric, but these won’t work with
$print().
-
+
strict_pos <- function(x) if (all(x > 0)) "yes" else "no"
fit$summary(variables = NULL, "Strictly Positive" = strict_pos)
variable Strictly Positive
@@ -283,7 +285,7 @@ Summary statistics
+
# fit$print(variables = NULL, "Strictly Positive" = strict_pos)
For more information, see posterior::summarise_draws(),
which is called by $summary().
@@ -297,49 +299,49 @@ Extracting posterior draws/samplesdraws_array
and
draws_df formats, but the posterior
package supports other useful formats as well.
-
+
# default is a 3-D draws_array object from the posterior package
# iterations x chains x variables
draws_arr <- fit$draws() # or format="array"
str(draws_arr)
- 'draws_array' num [1:1000, 1:4, 1:11] -57.1 -58.5 -56 -57.2 -58.5 ...
+ 'draws_array' num [1:1000, 1:4, 1:11] -61.7 -60.5 -61.8 -60.5 -66.8 ...
- attr(*, "dimnames")=List of 3
..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
..$ chain : chr [1:4] "1" "2" "3" "4"
..$ variable : chr [1:11] "lp__" "mu" "tau" "theta[1]" ...
-
+
# draws x variables data frame
draws_df <- fit$draws(format = "df")
str(draws_df)
draws_df [4,000 × 14] (S3: draws_df/draws/tbl_df/tbl/data.frame)
- $ lp__ : num [1:4000] -57.1 -58.5 -56 -57.2 -58.5 ...
- $ mu : num [1:4000] 9.17 4.62 11.15 5.67 5.37 ...
- $ tau : num [1:4000] 2.8 4.94 2.62 5.14 7.06 ...
- $ theta[1] : num [1:4000] 17.589 0.16 18.328 0.237 1.688 ...
- $ theta[2] : num [1:4000] 8.55 10.22 8.37 9.18 9.6 ...
- $ theta[3] : num [1:4000] 10.75 6.36 10.37 9.1 8.14 ...
- $ theta[4] : num [1:4000] 10.85 3.4 12.92 6.16 6.3 ...
- $ theta[5] : num [1:4000] 11 2.25 11.42 2.25 2.76 ...
- $ theta[6] : num [1:4000] 9.55 8.19 9.94 6.84 4.9 ...
- $ theta[7] : num [1:4000] 6.58 14.62 9.12 6.07 5.08 ...
- $ theta[8] : num [1:4000] 11.31 6.14 10.26 4.92 7.66 ...
+ $ lp__ : num [1:4000] -61.7 -60.5 -61.8 -60.5 -66.8 ...
+ $ mu : num [1:4000] 8.86 6.18 9.7 4.97 5.16 ...
+ $ tau : num [1:4000] 5.6 8.77 7.58 7.37 11.97 ...
+ $ theta[1] : num [1:4000] 16.716 14.062 6.67 6.25 -0.404 ...
+ $ theta[2] : num [1:4000] 4.377 6.173 15.608 0.638 10.449 ...
+ $ theta[3] : num [1:4000] 11.59 9.27 11.11 3.74 13.42 ...
+ $ theta[4] : num [1:4000] 13.68 12.51 7.77 6.48 10.16 ...
+ $ theta[5] : num [1:4000] -0.877 1.959 7.197 2.097 3.229 ...
+ $ theta[6] : num [1:4000] -5.02 -4.95 5.66 5.24 14.26 ...
+ $ theta[7] : num [1:4000] 13.38 17.44 13.69 6.65 13.77 ...
+ $ theta[8] : num [1:4000] 10.46 5.48 -6.15 20.92 31.79 ...
$ .chain : int [1:4000] 1 1 1 1 1 1 1 1 1 1 ...
$ .iteration: int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
$ .draw : int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
-
+
print(draws_df)
# A draws_df: 1000 iterations, 4 chains, and 11 variables
- lp__ mu tau theta[1] theta[2] theta[3] theta[4] theta[5]
-1 -57 9.17 2.8 17.59 8.5 10.75 10.8 11.0
-2 -58 4.62 4.9 0.16 10.2 6.36 3.4 2.3
-3 -56 11.15 2.6 18.33 8.4 10.37 12.9 11.4
-4 -57 5.67 5.1 0.24 9.2 9.10 6.2 2.3
-5 -59 5.37 7.1 1.69 9.6 8.14 6.3 2.8
-6 -59 0.64 4.4 6.20 6.6 0.44 4.1 2.4
-7 -53 7.85 3.0 6.58 6.7 8.34 7.4 6.7
-8 -54 8.05 3.9 7.66 6.5 4.54 7.1 8.3
-9 -54 6.80 3.3 7.75 8.5 1.37 6.6 9.4
-10 -59 5.09 5.8 0.40 9.4 -1.92 13.0 2.4
+ lp__ mu tau theta[1] theta[2] theta[3] theta[4] theta[5]
+1 -62 8.9 5.6 16.7 4.38 11.6 13.676 -0.88
+2 -61 6.2 8.8 14.1 6.17 9.3 12.515 1.96
+3 -62 9.7 7.6 6.7 15.61 11.1 7.770 7.20
+4 -60 5.0 7.4 6.3 0.64 3.7 6.477 2.10
+5 -67 5.2 12.0 -0.4 10.45 13.4 10.162 3.23
+6 -63 11.1 7.9 24.6 11.68 18.8 -1.606 3.79
+7 -61 14.7 6.1 28.9 11.15 15.3 6.780 9.95
+8 -63 13.4 5.7 13.7 21.03 7.2 20.051 12.99
+9 -64 16.0 8.3 20.2 2.65 11.3 19.000 3.51
+10 -59 2.9 4.5 4.2 11.37 2.5 -0.057 6.30
# ... with 3990 more draws, and 3 more variables
# ... hidden reserved variables {'.chain', '.iteration', '.draw'}
To convert an existing draws object to a different format use the
diff --git a/docs/articles/profiling.html b/docs/articles/profiling.html
index 613e6d86..dfbed9b1 100644
--- a/docs/articles/profiling.html
+++ b/docs/articles/profiling.html
@@ -250,11 +250,11 @@
Accessing the profiling info
fit$profiles()
[[1]]
name thread_id total_time forward_time reverse_time chain_stack
-1 likelihood 0x10cc54e00 0.76348000 0.62696000 0.13652000 51132
-2 priors 0x10cc54e00 0.00513667 0.00296958 0.00216709 34088
+1 likelihood 0x118759e00 0.69970800 0.57529800 0.12441000 51468
+2 priors 0x118759e00 0.00446571 0.00270154 0.00176417 34312
no_chain_stack autodiff_calls no_autodiff_calls
-1 34105044 17044 1
-2 34088 17044 1
+1 34329156 17156 1
+2 34312 17156 1
The total_time column is the total time spent inside a
given profile statement. It is clear that the vast majority of time is
spent in the likelihood function.
@@ -297,11 +297,11 @@ Comparing to a faster versio
fit_glm$profiles()
[[1]]
name thread_id total_time forward_time reverse_time chain_stack
-1 likelihood 0x115a87e00 0.39845200 0.39713500 0.00131726 49845
-2 priors 0x115a87e00 0.00381033 0.00234721 0.00146311 33230
+1 likelihood 0x106ce4e00 0.38400000 0.38264800 0.00135175 52203
+2 priors 0x106ce4e00 0.00356564 0.00237053 0.00119511 34802
no_chain_stack autodiff_calls no_autodiff_calls
-1 16615 16615 1
-2 33230 16615 1
+1 17401 17401 1
+2 34802 17401 1
We can see from the total_time column that this is much
faster than the previous model.
@@ -324,7 +324,7 @@ Per-gradient timings, and memory
profile_chain_1 <- fit$profiles()[[1]]
per_gradient_timing <- profile_chain_1$total_time/profile_chain_1$autodiff_calls
print(per_gradient_timing) # two elements for the two profile statements in the model
-[1] 4.479465e-05 3.013770e-07
+[1] 4.078503e-05 2.603002e-07
Accessing and saving the profile files
@@ -335,7 +335,7 @@ Accessing and saving the profile
$profile_files().
fit$profile_files()
-[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI4doTK/model_6580008f67848265f3dfd0e7ae3b0600-profile-202311081036-1-4a8018.csv"
+[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpJRtaoq/model_6580008f67848265f3dfd0e7ae3b0600-profile-202312131010-1-95e407.csv"
These can be saved to a more permanent location with the
$save_profile_files() method.
diff --git a/docs/index.html b/docs/index.html
index cc3f7698..1e06b71e 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -156,7 +156,7 @@ Goals
A clean interface to Stan services so that CmdStanR can keep up with Stan releases.
R code that doesn’t interface directly with C++, only calls compiled executables.
Modularity: CmdStanR runs Stan’s algorithms and lets downstream modules do the analysis.
Flexible BSD-3 license.
Flexible BSD-3 license.
License
CmdStanR, like CmdStan and the core Stan C++ code, is licensed under the following licenses:
-- Code: BSD 3-clause (https://opensource.org/licenses/BSD-3-Clause)
+- Code: BSD 3-clause (https://opensource.org/license/bsd-3-clause/)
- Documentation: CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
@@ -216,6 +216,12 @@ License
+
+Community
+
+
Citation
diff --git a/docs/news/index.html b/docs/news/index.html
index 64016e37..f702a044 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -123,6 +123,9 @@ Other improvements and bug fixes
Add support/tests for exposing functions with tuples by @andrjohns in #860
Add support/tests for exporting functions with complex types by @andrjohns in #861
Add option for installing from release archive by @andrjohns in #866
+Improve Pathfinder doc by @avehtari in #875
+Rename jacobian_adjustment argument to jacobian by @jgabry in #879
+Fix get_cmdstan_flags(‘STANCFLAGS’) in recursive make by @pearsonca in #881
@@ -312,7 +315,7 @@ Bug fixes
New features
-CSV reading is now faster by using data.table::fread(). (#318)
CSV reading is now faster by using data.table::fread(). (#318)
install_cmdstan() gains argument version for specifying which version of CmdStan to install. (#300, #308)
New function check_cmdstan_toolchain() that checks if the appropriate toolchains are available. (#289)
$sample() method for CmdStanModel objects gains argument chain_ids for specifying custom chain IDs. (#319)
Examples
# retrieve the gradients
test$gradients()
#> param_idx value model finite_diff error
-#> 1 0 0.797265 -8.06234 -8.06234 -9.72013e-09
-#> 2 1 0.216729 -15.64540 -15.64540 1.12721e-08
-#> 3 2 -0.207005 -4.61097 -4.61097 -5.25634e-09
-#> 4 3 1.118110 -6.60883 -6.60883 -2.24051e-08
+#> 1 0 -1.501560 34.56550 34.56550 4.58586e-08
+#> 2 1 -1.199130 1.69981 1.69981 -1.08299e-09
+#> 3 2 1.255420 -23.66640 -23.66640 -2.23140e-08
+#> 4 3 -0.452023 12.80740 12.80740 -8.57077e-09
# }
diff --git a/docs/reference/CmdStanGQ.html b/docs/reference/CmdStanGQ.html
index 28c7f7a4..6506d9af 100644
--- a/docs/reference/CmdStanGQ.html
+++ b/docs/reference/CmdStanGQ.html
@@ -177,7 +177,7 @@ Examples
#>
#> All 4 chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.6 seconds.
+#> Total execution time: 0.7 seconds.
#>
# stan program for standalone generated quantities
diff --git a/docs/reference/CmdStanModel.html b/docs/reference/CmdStanModel.html
index a309a128..6b2ba8d7 100644
--- a/docs/reference/CmdStanModel.html
+++ b/docs/reference/CmdStanModel.html
@@ -333,7 +333,7 @@ Examples
#> 6 -5.00402 0.000103557 2.55661e-07 1 1 9
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
-#> Finished in 0.1 seconds.
+#> Finished in 0.2 seconds.
fit_optim$summary()
#> # A tibble: 2 × 2
#> variable estimate
@@ -341,12 +341,12 @@ Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -6.94684
+#> Initial log joint probability = -6.92942
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 4 -6.74802 0.000319208 6.44211e-05 0.9206 0.9206 7
+#> 4 -6.74802 0.000269426 5.11368e-05 0.9249 0.9249 7
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -377,11 +377,11 @@ Examples
#> Finished in 0.1 seconds.
fit_laplace$summary()
#> # A tibble: 3 × 7
-#> variable mean median sd mad q5 q95
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.23 -6.96 0.692 0.298 -8.50 -6.75
-#> 2 lp_approx__ -0.490 -0.226 0.691 0.313 -1.88 -0.00180
-#> 3 theta 0.267 0.249 0.122 0.119 0.102 0.497
+#> variable mean median sd mad q5 q95
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -7.22 -6.97 0.652 0.296 -8.51 -6.75
+#> 2 lp_approx__ -0.492 -0.223 0.677 0.302 -1.91 -0.00222
+#> 3 theta 0.265 0.246 0.120 0.122 0.0952 0.484
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -450,6 +450,53 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -6.860971
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 7.999e-04 6.710e-06 1.000e+00 1.000e+00 101 -6.266e+00 -6.266e+00
+#> Path [1] :Best Iter: [2] ELBO (-6.159468) evaluations: (101)
+#> Path [2] :Initial log joint density = -15.050783
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.035e-03 6.058e-05 1.000e+00 1.000e+00 126 -6.231e+00 -6.231e+00
+#> Path [2] :Best Iter: [2] ELBO (-6.179167) evaluations: (126)
+#> Path [3] :Initial log joint density = -12.609181
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.519e-03 3.222e-05 1.000e+00 1.000e+00 126 -6.224e+00 -6.224e+00
+#> Path [3] :Best Iter: [3] ELBO (-6.177380) evaluations: (126)
+#> Path [4] :Initial log joint density = -7.551395
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.242e-04 5.362e-07 1.000e+00 1.000e+00 126 -6.267e+00 -6.267e+00
+#> Path [4] :Best Iter: [4] ELBO (-6.163367) evaluations: (126)
+#> Path [5] :Initial log joint density = -7.623161
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.410e-04 6.577e-07 1.000e+00 1.000e+00 126 -6.242e+00 -6.242e+00
+#> Path [5] :Best Iter: [5] ELBO (-6.241791) evaluations: (126)
+#> Path [6] :Initial log joint density = -12.604877
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.517e-03 3.214e-05 1.000e+00 1.000e+00 126 -6.220e+00 -6.220e+00
+#> Path [6] :Best Iter: [4] ELBO (-6.150438) evaluations: (126)
+#> Path [7] :Initial log joint density = -6.754804
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 1.079e-03 3.589e-05 1.000e+00 1.000e+00 76 -6.230e+00 -6.230e+00
+#> Path [7] :Best Iter: [2] ELBO (-6.228353) evaluations: (76)
+#> Path [8] :Initial log joint density = -6.839688
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 6.089e-04 4.257e-06 1.000e+00 1.000e+00 101 -6.279e+00 -6.279e+00
+#> Path [8] :Best Iter: [3] ELBO (-6.230942) evaluations: (101)
+#> Path [9] :Initial log joint density = -7.590990
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.335e-04 6.020e-07 1.000e+00 1.000e+00 126 -6.260e+00 -6.260e+00
+#> Path [9] :Best Iter: [4] ELBO (-6.210494) evaluations: (126)
+#> Path [10] :Initial log joint density = -11.878141
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.194e-03 2.066e-05 1.000e+00 1.000e+00 126 -6.255e+00 -6.255e+00
+#> Path [10] :Best Iter: [2] ELBO (-6.222876) evaluations: (126)
+#> Total log probability function evaluations:1310
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
@@ -516,7 +563,7 @@ Examples
#>
#> Both chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.4 seconds.
+#> Total execution time: 0.3 seconds.
#>
fit_optim_w_init_list <- mod$optimize(
data = stan_data,
diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png
index 6823093a..17a35806 100644
Binary files a/docs/reference/Rplot001.png and b/docs/reference/Rplot001.png differ
diff --git a/docs/reference/as_draws.CmdStanMCMC.html b/docs/reference/as_draws.CmdStanMCMC.html
index 40958e24..ab9c18ec 100644
--- a/docs/reference/as_draws.CmdStanMCMC.html
+++ b/docs/reference/as_draws.CmdStanMCMC.html
@@ -158,41 +158,41 @@ Examples
#>
#> chain
#> iteration 1 2 3 4
-#> 1 -66 -68 -66 -65
-#> 2 -65 -67 -65 -66
-#> 3 -68 -66 -65 -66
-#> 4 -68 -65 -65 -67
-#> 5 -67 -64 -65 -67
+#> 1 -66 -67 -65 -67
+#> 2 -65 -65 -64 -67
+#> 3 -66 -66 -65 -64
+#> 4 -65 -65 -65 -66
+#> 5 -67 -67 -66 -65
#>
#> , , variable = alpha
#>
#> chain
#> iteration 1 2 3 4
-#> 1 0.44 0.414 0.71 0.46
-#> 2 0.30 -0.045 0.44 0.17
-#> 3 0.65 0.397 0.14 0.55
-#> 4 0.29 0.644 0.47 0.54
-#> 5 0.53 0.435 0.48 0.20
+#> 1 0.37 0.0232 0.30 0.19
+#> 2 0.35 0.1359 0.50 0.56
+#> 3 0.39 0.0021 0.22 0.42
+#> 4 0.42 0.2264 0.22 0.21
+#> 5 0.30 0.6367 0.19 0.24
#>
#> , , variable = beta[1]
#>
#> chain
#> iteration 1 2 3 4
-#> 1 -0.91 -0.17 -0.50 -0.59
-#> 2 -0.57 -0.97 -0.78 -0.65
-#> 3 -0.90 -0.28 -0.38 -0.43
-#> 4 -1.20 -0.58 -0.92 -1.04
-#> 5 -0.59 -0.74 -0.70 -0.98
+#> 1 -0.21 -0.70 -0.87 -0.93
+#> 2 -0.42 -0.81 -0.58 -0.38
+#> 3 -1.01 -0.83 -0.81 -0.49
+#> 4 -0.95 -0.45 -0.67 -1.03
+#> 5 -0.16 -0.96 -0.73 -0.89
#>
#> , , variable = beta[2]
#>
#> chain
-#> iteration 1 2 3 4
-#> 1 0.088 -0.192 -0.43 -0.621
-#> 2 -0.515 -0.088 -0.35 -0.591
-#> 3 -0.031 -0.444 -0.11 0.017
-#> 4 -0.653 -0.229 -0.37 0.066
-#> 5 -0.345 -0.277 -0.13 -0.678
+#> iteration 1 2 3 4
+#> 1 -0.3841 0.08 -0.574 -0.73
+#> 2 -0.2843 -0.27 -0.376 0.12
+#> 3 -0.0223 -0.13 -0.065 -0.11
+#> 4 0.0016 -0.28 -0.100 -0.29
+#> 5 -0.5327 -0.66 -0.644 -0.16
#>
#> # ... with 995 more iterations, and 101 more variables
@@ -200,70 +200,71 @@ Examples
posterior::as_draws_rvars(fit)
#> # A draws_rvars: 1000 iterations, 4 chains, and 4 variables
#> $lp__: rvar<1000,4>[1] mean ± sd:
-#> [1] -66 ± 1.5
+#> [1] -66 ± 1.4
#>
#> $alpha: rvar<1000,4>[1] mean ± sd:
-#> [1] 0.38 ± 0.22
+#> [1] 0.37 ± 0.22
#>
#> $beta: rvar<1000,4>[3] mean ± sd:
-#> [1] -0.67 ± 0.26 -0.27 ± 0.23 0.69 ± 0.27
+#> [1] -0.66 ± 0.25 -0.27 ± 0.23 0.67 ± 0.26
#>
#> $log_lik: rvar<1000,4>[100] mean ± sd:
-#> [1] -0.517 ± 0.098 -0.401 ± 0.152 -0.501 ± 0.223 -0.448 ± 0.154
-#> [5] -1.185 ± 0.289 -0.590 ± 0.195 -0.640 ± 0.126 -0.278 ± 0.135
-#> [9] -0.693 ± 0.170 -0.742 ± 0.238 -0.279 ± 0.127 -0.494 ± 0.244
-#> [13] -0.659 ± 0.215 -0.363 ± 0.176 -0.279 ± 0.108 -0.275 ± 0.087
-#> [17] -1.593 ± 0.288 -0.479 ± 0.107 -0.232 ± 0.076 -0.113 ± 0.080
-#> [21] -0.211 ± 0.089 -0.568 ± 0.149 -0.330 ± 0.140 -0.136 ± 0.067
-#> [25] -0.454 ± 0.123 -1.517 ± 0.342 -0.306 ± 0.123 -0.445 ± 0.083
-#> [29] -0.725 ± 0.232 -0.696 ± 0.191 -0.487 ± 0.164 -0.423 ± 0.109
-#> [33] -0.409 ± 0.130 -0.064 ± 0.052 -0.587 ± 0.189 -0.325 ± 0.132
-#> [37] -0.700 ± 0.231 -0.312 ± 0.151 -0.180 ± 0.111 -0.681 ± 0.128
-#> [41] -1.133 ± 0.258 -0.932 ± 0.200 -0.416 ± 0.265 -1.175 ± 0.185
-#> [45] -0.360 ± 0.118 -0.580 ± 0.131 -0.303 ± 0.131 -0.324 ± 0.082
-#> [49] -0.319 ± 0.079 -1.285 ± 0.333 -0.289 ± 0.094 -0.832 ± 0.142
-#> [53] -0.401 ± 0.131 -0.373 ± 0.143 -0.384 ± 0.139 -0.321 ± 0.192
-#> [57] -0.658 ± 0.119 -0.951 ± 0.363 -1.364 ± 0.348 -0.976 ± 0.158
-#> [61] -0.542 ± 0.097 -0.871 ± 0.318 -0.116 ± 0.075 -0.901 ± 0.250
-#> [65] -2.026 ± 0.610 -0.508 ± 0.138 -0.276 ± 0.082 -1.061 ± 0.237
-#> [69] -0.435 ± 0.083 -0.641 ± 0.235 -0.613 ± 0.214 -0.464 ± 0.177
-#> [73] -1.489 ± 0.374 -0.948 ± 0.198 -1.136 ± 0.394 -0.374 ± 0.140
-#> [77] -0.878 ± 0.139 -0.491 ± 0.174 -0.765 ± 0.194 -0.539 ± 0.200
-#> [81] -0.163 ± 0.102 -0.223 ± 0.145 -0.343 ± 0.080 -0.275 ± 0.092
-#> [85] -0.130 ± 0.075 -1.132 ± 0.322 -0.822 ± 0.126 -0.780 ± 0.249
-#> [89] -1.283 ± 0.323 -0.261 ± 0.137 -0.387 ± 0.133 -1.501 ± 0.352
-#> [93] -0.737 ± 0.221 -0.318 ± 0.087 -0.388 ± 0.112 -1.573 ± 0.284
-#> [97] -0.432 ± 0.100 -1.053 ± 0.383 -0.693 ± 0.143 -0.392 ± 0.096
+#> [1] -0.518 ± 0.099 -0.405 ± 0.147 -0.501 ± 0.220 -0.451 ± 0.153
+#> [5] -1.173 ± 0.276 -0.592 ± 0.185 -0.640 ± 0.123 -0.280 ± 0.135
+#> [9] -0.693 ± 0.163 -0.740 ± 0.234 -0.283 ± 0.124 -0.500 ± 0.241
+#> [13] -0.655 ± 0.206 -0.364 ± 0.175 -0.282 ± 0.106 -0.278 ± 0.087
+#> [17] -1.582 ± 0.288 -0.481 ± 0.107 -0.235 ± 0.076 -0.115 ± 0.079
+#> [21] -0.215 ± 0.088 -0.570 ± 0.144 -0.332 ± 0.140 -0.139 ± 0.067
+#> [25] -0.456 ± 0.121 -1.513 ± 0.345 -0.309 ± 0.120 -0.447 ± 0.083
+#> [29] -0.722 ± 0.227 -0.694 ± 0.189 -0.489 ± 0.157 -0.427 ± 0.106
+#> [33] -0.412 ± 0.126 -0.065 ± 0.052 -0.585 ± 0.184 -0.327 ± 0.135
+#> [37] -0.700 ± 0.227 -0.314 ± 0.150 -0.182 ± 0.110 -0.681 ± 0.126
+#> [41] -1.124 ± 0.248 -0.928 ± 0.195 -0.412 ± 0.267 -1.169 ± 0.185
+#> [45] -0.362 ± 0.119 -0.583 ± 0.128 -0.307 ± 0.129 -0.327 ± 0.084
+#> [49] -0.322 ± 0.079 -1.286 ± 0.334 -0.291 ± 0.096 -0.833 ± 0.144
+#> [53] -0.404 ± 0.127 -0.373 ± 0.143 -0.388 ± 0.134 -0.322 ± 0.195
+#> [57] -0.658 ± 0.117 -0.951 ± 0.353 -1.351 ± 0.337 -0.975 ± 0.159
+#> [61] -0.542 ± 0.099 -0.876 ± 0.314 -0.119 ± 0.075 -0.901 ± 0.247
+#> [65] -1.995 ± 0.581 -0.511 ± 0.134 -0.279 ± 0.083 -1.057 ± 0.233
+#> [69] -0.438 ± 0.083 -0.638 ± 0.235 -0.609 ± 0.207 -0.463 ± 0.169
+#> [73] -1.476 ± 0.363 -0.944 ± 0.194 -1.139 ± 0.389 -0.376 ± 0.140
+#> [77] -0.875 ± 0.136 -0.489 ± 0.174 -0.764 ± 0.190 -0.545 ± 0.196
+#> [81] -0.165 ± 0.100 -0.226 ± 0.138 -0.346 ± 0.081 -0.278 ± 0.091
+#> [85] -0.132 ± 0.075 -1.120 ± 0.315 -0.822 ± 0.127 -0.773 ± 0.237
+#> [89] -1.270 ± 0.314 -0.263 ± 0.136 -0.388 ± 0.129 -1.484 ± 0.335
+#> [93] -0.738 ± 0.218 -0.321 ± 0.088 -0.391 ± 0.110 -1.562 ± 0.280
+#> [97] -0.433 ± 0.102 -1.052 ± 0.372 -0.693 ± 0.139 -0.393 ± 0.098
#>
posterior::as_draws_list(fit)
#> # A draws_list: 1000 iterations, 4 chains, and 105 variables
#>
#> [chain = 1]
#> $lp__
-#> [1] -66 -65 -68 -68 -67 -66 -65 -66 -67 -66
+#> [1] -66 -65 -66 -65 -67 -65 -65 -64 -67 -69
#>
#> $alpha
-#> [1] 0.44 0.30 0.65 0.29 0.53 0.53 0.47 0.38 0.32 0.51
+#> [1] 0.373 0.355 0.389 0.419 0.300 0.178 0.629 0.288 0.016 -0.075
#>
#> $`beta[1]`
-#> [1] -0.91 -0.57 -0.90 -1.20 -0.59 -0.51 -0.36 -0.19 -1.17 -1.02
+#> [1] -0.21 -0.42 -1.01 -0.95 -0.16 -0.51 -0.69 -0.67 -0.74 -0.71
#>
#> $`beta[2]`
-#> [1] 0.088 -0.515 -0.031 -0.653 -0.345 -0.646 -0.154 -0.488 -0.013 0.034
+#> [1] -0.3841 -0.2843 -0.0223 0.0016 -0.5327 -0.3040 -0.3392 -0.2792 0.0976
+#> [10] 0.1916
#>
#>
#> [chain = 2]
#> $lp__
-#> [1] -68 -67 -66 -65 -64 -65 -65 -64 -65 -65
+#> [1] -67 -65 -66 -65 -67 -67 -67 -64 -68 -66
#>
#> $alpha
-#> [1] 0.414 -0.045 0.397 0.644 0.435 0.266 0.415 0.369 0.229 0.185
+#> [1] 0.0232 0.1359 0.0021 0.2264 0.6367 0.7152 0.4707 0.3940 0.0634 0.4544
#>
#> $`beta[1]`
-#> [1] -0.17 -0.97 -0.28 -0.58 -0.74 -0.48 -0.83 -0.72 -0.44 -0.58
+#> [1] -0.70 -0.81 -0.83 -0.45 -0.96 -0.34 -1.17 -0.57 -0.62 -0.62
#>
#> $`beta[2]`
-#> [1] -0.192 -0.088 -0.444 -0.229 -0.277 -0.314 0.046 -0.240 -0.080 -0.090
+#> [1] 0.08 -0.27 -0.13 -0.28 -0.66 -0.57 -0.21 -0.20 -0.47 -0.67
#>
#> # ... with 990 more iterations, and 2 more chains, and 101 more variables
# }
diff --git a/docs/reference/cmdstan_model.html b/docs/reference/cmdstan_model.html
index 9385aa48..a94e571b 100644
--- a/docs/reference/cmdstan_model.html
+++ b/docs/reference/cmdstan_model.html
@@ -358,12 +358,12 @@ Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -6.79903
+#> Initial log joint probability = -17.5452
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 3 -6.74802 0.00379849 4.77236e-05 0.9516 0.9516 6
+#> 5 -6.74802 0.00115356 2.90728e-05 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -394,11 +394,11 @@ Examples
#> Finished in 0.1 seconds.
fit_laplace$summary()
#> # A tibble: 3 × 7
-#> variable mean median sd mad q5 q95
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.22 -6.97 0.659 0.305 -8.52 -6.75
-#> 2 lp_approx__ -0.481 -0.224 0.662 0.308 -1.84 -0.00186
-#> 3 theta 0.272 0.252 0.121 0.122 0.103 0.502
+#> variable mean median sd mad q5 q95
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -7.27 -7.00 0.705 0.338 -8.72 -6.75
+#> 2 lp_approx__ -0.527 -0.251 0.714 0.340 -2.02 -0.00142
+#> 3 theta 0.270 0.251 0.126 0.125 0.0959 0.509
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -407,8 +407,8 @@ Examples
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
-#> Gradient evaluation took 6e-06 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
+#> Gradient evaluation took 5e-06 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
@@ -467,6 +467,53 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -11.515672
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.064e-03 1.692e-05 1.000e+00 1.000e+00 126 -6.254e+00 -6.254e+00
+#> Path [1] :Best Iter: [3] ELBO (-6.213658) evaluations: (126)
+#> Path [2] :Initial log joint density = -9.189304
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 3.749e-04 3.327e-06 1.000e+00 1.000e+00 126 -6.289e+00 -6.289e+00
+#> Path [2] :Best Iter: [4] ELBO (-6.214534) evaluations: (126)
+#> Path [3] :Initial log joint density = -13.270061
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.767e-03 4.305e-05 1.000e+00 1.000e+00 126 -6.276e+00 -6.276e+00
+#> Path [3] :Best Iter: [3] ELBO (-6.225363) evaluations: (126)
+#> Path [4] :Initial log joint density = -6.748213
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 2 -6.748e+00 1.004e-02 7.315e-05 1.000e+00 1.000e+00 51 -6.200e+00 -6.200e+00
+#> Path [4] :Best Iter: [2] ELBO (-6.200114) evaluations: (51)
+#> Path [5] :Initial log joint density = -7.368682
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 5.377e-04 9.870e-07 1.000e+00 1.000e+00 126 -6.276e+00 -6.276e+00
+#> Path [5] :Best Iter: [3] ELBO (-6.167297) evaluations: (126)
+#> Path [6] :Initial log joint density = -9.465368
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 4.613e-04 4.599e-06 1.000e+00 1.000e+00 126 -6.238e+00 -6.238e+00
+#> Path [6] :Best Iter: [3] ELBO (-6.185454) evaluations: (126)
+#> Path [7] :Initial log joint density = -6.856712
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 1.028e-04 1.386e-05 9.452e-01 9.452e-01 101 -6.242e+00 -6.242e+00
+#> Path [7] :Best Iter: [3] ELBO (-6.159687) evaluations: (101)
+#> Path [8] :Initial log joint density = -7.085030
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 3.058e-03 6.268e-05 1.000e+00 1.000e+00 101 -6.221e+00 -6.221e+00
+#> Path [8] :Best Iter: [4] ELBO (-6.221460) evaluations: (101)
+#> Path [9] :Initial log joint density = -7.443992
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 9.951e-05 3.747e-07 1.000e+00 1.000e+00 126 -6.228e+00 -6.228e+00
+#> Path [9] :Best Iter: [2] ELBO (-6.206717) evaluations: (126)
+#> Path [10] :Initial log joint density = -6.751684
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 5.950e-04 1.461e-05 1.000e+00 1.000e+00 76 -6.209e+00 -6.209e+00
+#> Path [10] :Best Iter: [3] ELBO (-6.209430) evaluations: (76)
+#> Total log probability function evaluations:1235
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
diff --git a/docs/reference/cmdstanr-package.html b/docs/reference/cmdstanr-package.html
index 6e29914b..072311bb 100644
--- a/docs/reference/cmdstanr-package.html
+++ b/docs/reference/cmdstanr-package.html
@@ -291,7 +291,7 @@ Examples
#> [1] 0 0
#>
#> $ebfmi
-#> [1] 1.0 1.3
+#> [1] 1.017555 1.250490
#>
# Get posterior draws
@@ -364,12 +364,12 @@ Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -7.20447
+#> Initial log joint probability = -18.9282
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 4 -6.74802 0.00428356 0.000109727 1 1 7
+#> 5 -6.74802 0.00032939 4.62604e-06 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -397,14 +397,14 @@ Examples
#> iteration: 1700
#> iteration: 1800
#> iteration: 1900
-#> Finished in 0.2 seconds.
+#> Finished in 0.1 seconds.
fit_laplace$summary()
#> # A tibble: 3 × 7
#> variable mean median sd mad q5 q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.23 -6.96 0.694 0.287 -8.53 -6.75
-#> 2 lp_approx__ -0.493 -0.211 0.714 0.286 -1.87 -0.00222
-#> 3 theta 0.264 0.248 0.120 0.120 0.0954 0.491
+#> 1 lp__ -7.23 -6.97 0.683 0.304 -8.57 -6.75
+#> 2 lp_approx__ -0.496 -0.225 0.743 0.305 -1.91 -0.00217
+#> 3 theta 0.264 0.247 0.120 0.119 0.0967 0.492
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -473,6 +473,53 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -12.637372
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.531e-03 3.268e-05 1.000e+00 1.000e+00 126 -6.214e+00 -6.214e+00
+#> Path [1] :Best Iter: [5] ELBO (-6.213940) evaluations: (126)
+#> Path [2] :Initial log joint density = -11.196746
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 9.877e-04 1.507e-05 1.000e+00 1.000e+00 126 -6.283e+00 -6.283e+00
+#> Path [2] :Best Iter: [4] ELBO (-6.223987) evaluations: (126)
+#> Path [3] :Initial log joint density = -10.337120
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 7.410e-04 9.634e-06 1.000e+00 1.000e+00 126 -6.236e+00 -6.236e+00
+#> Path [3] :Best Iter: [2] ELBO (-6.179583) evaluations: (126)
+#> Path [4] :Initial log joint density = -12.005851
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.253e-03 2.257e-05 1.000e+00 1.000e+00 126 -6.159e+00 -6.159e+00
+#> Path [4] :Best Iter: [5] ELBO (-6.159209) evaluations: (126)
+#> Path [5] :Initial log joint density = -11.377707
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.032e-03 1.614e-05 1.000e+00 1.000e+00 126 -6.206e+00 -6.206e+00
+#> Path [5] :Best Iter: [2] ELBO (-6.202552) evaluations: (126)
+#> Path [6] :Initial log joint density = -7.049866
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 2.694e-03 5.075e-05 1.000e+00 1.000e+00 101 -6.203e+00 -6.203e+00
+#> Path [6] :Best Iter: [4] ELBO (-6.203218) evaluations: (101)
+#> Path [7] :Initial log joint density = -9.219130
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 3.841e-04 3.455e-06 1.000e+00 1.000e+00 126 -6.185e+00 -6.185e+00
+#> Path [7] :Best Iter: [5] ELBO (-6.185156) evaluations: (126)
+#> Path [8] :Initial log joint density = -17.192426
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.354e-03 3.651e-05 1.000e+00 1.000e+00 126 -6.204e+00 -6.204e+00
+#> Path [8] :Best Iter: [4] ELBO (-6.168586) evaluations: (126)
+#> Path [9] :Initial log joint density = -9.491732
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 4.697e-04 4.731e-06 1.000e+00 1.000e+00 126 -6.247e+00 -6.247e+00
+#> Path [9] :Best Iter: [3] ELBO (-6.220590) evaluations: (126)
+#> Path [10] :Initial log joint density = -6.750036
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 3.322e-04 6.069e-06 1.000e+00 1.000e+00 76 -6.214e+00 -6.214e+00
+#> Path [10] :Best Iter: [2] ELBO (-6.188430) evaluations: (76)
+#> Total log probability function evaluations:1335
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
@@ -488,7 +535,7 @@ Examples
#>
#> Both chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.7 seconds.
+#> Total execution time: 0.3 seconds.
#>
fit_mcmc_w_init_fun_2 <- mod$sample(
data = stan_data,
@@ -517,7 +564,7 @@ Examples
#>
#> [[2]]
#> [[2]]$theta
-#> [1] 0.33
+#> [1] 0.3333333
#>
#>
@@ -539,7 +586,7 @@ Examples
#>
#> Both chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.5 seconds.
+#> Total execution time: 0.3 seconds.
#>
fit_optim_w_init_list <- mod$optimize(
data = stan_data,
diff --git a/docs/reference/cmdstanr_example.html b/docs/reference/cmdstanr_example.html
index babf2ce0..8d377b8f 100644
--- a/docs/reference/cmdstanr_example.html
+++ b/docs/reference/cmdstanr_example.html
@@ -187,18 +187,18 @@ Examples
fit_logistic_mcmc <- cmdstanr_example("logistic", chains = 2)
fit_logistic_mcmc$summary()
#> # A tibble: 105 × 10
-#> variable mean median sd mad q5 q95 rhat ess_bulk
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -66.1 -65.7 1.53 1.26 -69.1 -64.3 1.00 1090.
-#> 2 alpha 0.373 0.371 0.223 0.215 0.0169 0.729 1.00 1944.
-#> 3 beta[1] -0.678 -0.676 0.260 0.261 -1.11 -0.265 1.00 1936.
-#> 4 beta[2] -0.276 -0.274 0.234 0.239 -0.674 0.0978 1.00 1841.
-#> 5 beta[3] 0.684 0.681 0.268 0.270 0.270 1.13 1.00 2070.
-#> 6 log_lik[1] -0.518 -0.509 0.102 0.101 -0.694 -0.366 0.999 1983.
-#> 7 log_lik[2] -0.398 -0.380 0.149 0.143 -0.665 -0.194 1.00 2108.
-#> 8 log_lik[3] -0.501 -0.461 0.228 0.210 -0.939 -0.204 1.00 1995.
-#> 9 log_lik[4] -0.446 -0.428 0.157 0.148 -0.740 -0.226 1.00 1806.
-#> 10 log_lik[5] -1.18 -1.15 0.285 0.286 -1.67 -0.754 1.00 2253.
+#> variable mean median sd mad q5 q95 rhat ess_bulk
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -66.0 -65.6 1.45 1.20 -68.7 -64.3 1.00 934.
+#> 2 alpha 0.384 0.386 0.218 0.218 0.0341 0.748 1.00 1959.
+#> 3 beta[1] -0.667 -0.661 0.247 0.248 -1.08 -0.273 1.00 1840.
+#> 4 beta[2] -0.275 -0.273 0.231 0.231 -0.662 0.114 1.00 1954.
+#> 5 beta[3] 0.687 0.685 0.271 0.269 0.254 1.13 1.00 2212.
+#> 6 log_lik[1] -0.515 -0.509 0.101 0.0967 -0.698 -0.360 1.00 2038.
+#> 7 log_lik[2] -0.402 -0.380 0.150 0.140 -0.673 -0.195 1.00 2565.
+#> 8 log_lik[3] -0.498 -0.467 0.223 0.212 -0.922 -0.201 1.00 2174.
+#> 9 log_lik[4] -0.449 -0.430 0.154 0.148 -0.731 -0.232 0.999 1785.
+#> 10 log_lik[5] -1.19 -1.17 0.283 0.281 -1.69 -0.772 1.00 2119.
#> # ℹ 95 more rows
#> # ℹ 1 more variable: ess_tail <dbl>
@@ -211,7 +211,7 @@ Examples
#> 2 alpha 0.364
#> 3 beta[1] -0.632
#> 4 beta[2] -0.259
-#> 5 beta[3] 0.649
+#> 5 beta[3] 0.648
#> 6 log_lik[1] -0.515
#> 7 log_lik[2] -0.394
#> 8 log_lik[3] -0.469
@@ -222,18 +222,18 @@ Examples
fit_logistic_vb <- cmdstanr_example("logistic", method = "variational")
fit_logistic_vb$summary()
#> # A tibble: 106 × 7
-#> variable mean median sd mad q5 q95
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -66.5 -66.1 1.98 1.74 -70.5 -64.4
-#> 2 lp_approx__ -2.01 -1.63 1.53 1.27 -5.07 -0.341
-#> 3 alpha 0.305 0.311 0.248 0.234 -0.116 0.716
-#> 4 beta[1] -0.623 -0.633 0.230 0.239 -0.987 -0.255
-#> 5 beta[2] -0.377 -0.369 0.263 0.258 -0.821 0.0339
-#> 6 beta[3] 0.681 0.671 0.291 0.288 0.215 1.16
-#> 7 log_lik[1] -0.565 -0.558 0.118 0.108 -0.766 -0.383
-#> 8 log_lik[2] -0.412 -0.388 0.172 0.156 -0.740 -0.186
-#> 9 log_lik[3] -0.641 -0.589 0.294 0.280 -1.22 -0.265
-#> 10 log_lik[4] -0.389 -0.367 0.145 0.134 -0.662 -0.190
+#> variable mean median sd mad q5 q95
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -66.5 -66.0 1.82 1.62 -69.9 -64.3
+#> 2 lp_approx__ -1.96 -1.62 1.41 1.19 -4.62 -0.318
+#> 3 alpha 0.503 0.492 0.219 0.222 0.144 0.876
+#> 4 beta[1] -0.582 -0.568 0.301 0.288 -1.10 -0.0916
+#> 5 beta[2] -0.278 -0.275 0.185 0.183 -0.592 0.0118
+#> 6 beta[3] 0.688 0.696 0.301 0.307 0.211 1.18
+#> 7 log_lik[1] -0.480 -0.475 0.0981 0.0994 -0.648 -0.332
+#> 8 log_lik[2] -0.466 -0.443 0.190 0.185 -0.836 -0.197
+#> 9 log_lik[3] -0.470 -0.443 0.195 0.188 -0.838 -0.209
+#> 10 log_lik[4] -0.495 -0.480 0.152 0.148 -0.776 -0.277
#> # ℹ 96 more rows
print_example_program("schools")
@@ -254,23 +254,23 @@ Examples
#> target += normal_lpdf(y | theta, sigma);
#> }
fit_schools_mcmc <- cmdstanr_example("schools")
-#> Warning: 91 of 4000 (2.0%) transitions ended with a divergence.
+#> Warning: 146 of 4000 (4.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.
fit_schools_mcmc$summary()
#> # A tibble: 11 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -57.8 -58.2 5.50 5.71 -66.3 -48.3 1.04 151. 248.
-#> 2 mu 6.47 6.50 4.26 4.17 -1.05 13.3 1.01 464. 141.
-#> 3 tau 5.22 4.49 3.59 3.40 0.984 12.2 1.04 115. 122.
-#> 4 theta[1] 9.21 8.56 7.05 6.14 -1.40 21.8 1.01 427. 239.
-#> 5 theta[2] 6.77 6.75 5.62 5.20 -2.79 15.9 1.01 718. 443.
-#> 6 theta[3] 5.35 5.83 6.82 5.87 -6.13 15.5 1.01 1103. 1908.
-#> 7 theta[4] 6.84 6.85 5.86 5.44 -2.42 16.6 1.01 923. 2076.
-#> 8 theta[5] 4.64 4.91 5.62 5.40 -4.88 13.4 1.01 775. 1603.
-#> 9 theta[6] 5.51 5.70 6.05 5.48 -4.39 15.1 1.01 639. 1878.
-#> 10 theta[7] 8.94 8.42 6.14 5.47 -0.805 19.6 1.02 272. 164.
-#> 11 theta[8] 7.08 6.94 6.75 5.70 -3.52 18.0 1.01 969. 1764.
+#> 1 lp__ -58.4 -58.5 5.06 5.42 -66.4 -50.2 1.02 139. 71.0
+#> 2 mu 6.36 6.39 4.24 4.27 -0.684 13.0 1.01 487. 345.
+#> 3 tau 5.42 4.61 3.53 3.33 1.25 12.3 1.02 136. 67.5
+#> 4 theta[1] 9.11 8.41 7.20 6.29 -1.35 21.8 1.01 774. 601.
+#> 5 theta[2] 6.65 6.69 5.88 5.59 -2.86 16.2 1.01 1011. 2093.
+#> 6 theta[3] 5.38 5.61 6.74 6.18 -5.83 15.9 1.01 863. 1953.
+#> 7 theta[4] 6.59 6.59 6.01 5.73 -2.94 16.6 1.01 946. 2109.
+#> 8 theta[5] 4.37 4.72 5.66 5.51 -5.63 12.8 1.01 745. 2276.
+#> 9 theta[6] 5.28 5.49 5.96 5.60 -4.99 14.6 1.01 879. 1751.
+#> 10 theta[7] 9.06 8.65 6.17 5.72 -0.151 20.1 1.01 678. 1072.
+#> 11 theta[8] 6.91 6.84 6.86 6.09 -4.10 17.8 1.01 939. 2286.
print_example_program("schools_ncp")
#> data {
@@ -295,36 +295,35 @@ Examples
fit_schools_ncp_mcmc <- cmdstanr_example("schools_ncp")
fit_schools_ncp_mcmc$summary()
#> # A tibble: 19 × 10
-#> variable mean median sd mad q5 q95 rhat ess_bulk
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -46.9 -46.6 2.47 2.39 -51.5 -43.5 1.00 1443.
-#> 2 mu 6.50 6.57 4.25 4.13 -0.446 13.4 1.00 2941.
-#> 3 tau 4.78 3.91 3.74 3.46 0.374 12.1 1.00 1743.
-#> 4 theta_raw[1] 0.372 0.381 0.989 0.951 -1.28 1.97 1.00 3537.
-#> 5 theta_raw[2] 0.0565 0.0620 0.915 0.928 -1.45 1.53 1.00 4450.
-#> 6 theta_raw[3] -0.148 -0.163 0.938 0.932 -1.65 1.40 1.00 3407.
-#> 7 theta_raw[4] 0.00702 0.00718 0.937 0.912 -1.55 1.53 1.00 3544.
-#> 8 theta_raw[5] -0.264 -0.271 0.896 0.870 -1.70 1.23 1.00 3495.
-#> 9 theta_raw[6] -0.148 -0.157 0.950 0.938 -1.68 1.43 1.00 4517.
-#> 10 theta_raw[7] 0.351 0.374 0.924 0.876 -1.23 1.82 1.00 3596.
-#> 11 theta_raw[8] 0.0685 0.0540 0.960 0.970 -1.52 1.64 1.00 3657.
-#> 12 theta[1] 8.96 8.30 6.79 5.56 -0.975 21.5 1.00 3374.
-#> 13 theta[2] 6.90 6.72 5.56 5.25 -2.07 16.0 1.00 4663.
-#> 14 theta[3] 5.58 5.91 6.35 5.42 -5.10 15.2 1.00 3392.
-#> 15 theta[4] 6.67 6.72 6.02 5.40 -2.96 16.2 1.00 3971.
-#> 16 theta[5] 4.81 5.19 5.50 5.21 -4.81 12.9 1.00 4119.
-#> 17 theta[6] 5.55 5.86 5.93 5.29 -4.38 14.7 1.00 4010.
-#> 18 theta[7] 8.73 8.24 5.88 5.28 -0.0503 19.3 1.00 3955.
-#> 19 theta[8] 7.01 6.84 6.39 5.49 -3.28 17.9 1.00 3861.
-#> # ℹ 1 more variable: ess_tail <dbl>
+#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -46.9 -4.66e+1 2.49 2.36 -51.6 -43.6 1.00 1229. 1844.
+#> 2 mu 6.43 6.46e+0 4.08 3.93 -0.238 13.1 1.00 2985. 2197.
+#> 3 tau 4.74 4.03e+0 3.70 3.41 0.334 11.9 1.00 1724. 1496.
+#> 4 theta_r… 0.351 3.53e-1 0.979 0.955 -1.26 1.96 1.00 3404. 2385.
+#> 5 theta_r… 0.0484 6.67e-2 0.924 0.913 -1.49 1.53 1.00 3680. 2435.
+#> 6 theta_r… -0.131 -1.50e-1 0.962 0.938 -1.71 1.50 1.00 3067. 2557.
+#> 7 theta_r… 0.0221 5.18e-3 0.919 0.905 -1.49 1.59 1.00 3748. 2583.
+#> 8 theta_r… -0.273 -3.14e-1 0.931 0.892 -1.77 1.34 1.00 4139. 2891.
+#> 9 theta_r… -0.157 -1.57e-1 0.930 0.929 -1.69 1.36 1.00 3560. 2797.
+#> 10 theta_r… 0.363 4.01e-1 0.924 0.911 -1.15 1.87 1.00 3687. 2941.
+#> 11 theta_r… 0.0558 7.25e-2 0.951 0.924 -1.55 1.65 1.00 4077. 2555.
+#> 12 theta[1] 8.85 8.09e+0 6.85 5.77 -0.638 21.2 1.00 2994. 2531.
+#> 13 theta[2] 6.79 6.75e+0 5.59 5.01 -2.46 15.9 1.00 4593. 3367.
+#> 14 theta[3] 5.40 5.88e+0 6.64 5.63 -5.94 15.5 1.00 3448. 3181.
+#> 15 theta[4] 6.65 6.62e+0 5.69 5.09 -2.25 16.0 1.00 4537. 3155.
+#> 16 theta[5] 4.78 5.06e+0 5.54 5.26 -4.69 13.2 1.00 3996. 3191.
+#> 17 theta[6] 5.44 5.63e+0 5.97 5.29 -4.66 14.8 1.00 3934. 2877.
+#> 18 theta[7] 8.83 8.37e+0 5.98 5.56 0.141 19.4 1.00 4104. 3336.
+#> 19 theta[8] 6.89 6.73e+0 6.34 5.33 -3.03 17.1 1.00 3988. 3025.
# optimization fails for hierarchical model
cmdstanr_example("schools", "optimize", quiet = FALSE)
-#> Initial log joint probability = -319.937
+#> Initial log joint probability = -56.2449
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 99 120.913 0.248985 3.76333e+09 1 1 183
+#> 99 121.706 0.109668 2.48002e+09 1 1 174
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 180 242.851 0.0892321 2.24452e+16 1e-12 0.001 378 LS failed, Hessian reset
+#> 187 244.785 0.253817 8.00296 1e-12 0.001 400 LS failed, Hessian reset
#> Line search failed to achieve a sufficient decrease, no more progress can be made
#> Chain 1 Optimization terminated with error:
#> Warning: Fitting finished unexpectedly! Use the $output() method for more information.
diff --git a/docs/reference/draws_to_csv.html b/docs/reference/draws_to_csv.html
index 9f523602..355224d7 100644
--- a/docs/reference/draws_to_csv.html
+++ b/docs/reference/draws_to_csv.html
@@ -169,10 +169,10 @@ Examples
draws_csv_files <- draws_to_csv(draws)
print(draws_csv_files)
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-1-7ea6a2.csv"
-#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-2-7ea6a2.csv"
-#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-3-7ea6a2.csv"
-#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-4-7ea6a2.csv"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpAMFHpW/fittedParams-202312131001-1-1dd4e6.csv"
+#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpAMFHpW/fittedParams-202312131001-2-1dd4e6.csv"
+#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpAMFHpW/fittedParams-202312131001-3-1dd4e6.csv"
+#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpAMFHpW/fittedParams-202312131001-4-1dd4e6.csv"
# draws_csv_files <- draws_to_csv(draws,
# sampler_diagnostic = sampler_diagnostics,
diff --git a/docs/reference/fit-method-cmdstan_summary.html b/docs/reference/fit-method-cmdstan_summary.html
index 2ab3dc62..47d7f8f7 100644
--- a/docs/reference/fit-method-cmdstan_summary.html
+++ b/docs/reference/fit-method-cmdstan_summary.html
@@ -149,7 +149,7 @@ Examples
# \dontrun{
fit <- cmdstanr_example("logistic")
fit$cmdstan_diagnose()
-#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-1-1f791d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-2-1f791d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-3-1f791d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-4-1f791d.csv
+#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-1-2718e9.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-2-2718e9.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-3-2718e9.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-4-2718e9.csv
#>
#> Checking sampler transitions treedepth.
#> Treedepth satisfactory for all transitions.
@@ -169,123 +169,123 @@ Examples
#> Inference for Stan model: logistic_model
#> 4 chains: each with iter=(1000,1000,1000,1000); warmup=(0,0,0,0); thin=(1,1,1,1); 4000 iterations saved.
#>
-#> Warmup took (0.023, 0.023, 0.023, 0.023) seconds, 0.092 seconds total
-#> Sampling took (0.076, 0.070, 0.072, 0.073) seconds, 0.29 seconds total
+#> Warmup took (0.022, 0.023, 0.023, 0.023) seconds, 0.091 seconds total
+#> Sampling took (0.071, 0.070, 0.070, 0.070) seconds, 0.28 seconds total
#>
#> Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
#>
-#> lp__ -6.6e+01 3.0e-02 1.4 -69 -6.6e+01 -6.4e+01 2277 7826 1.0
-#> accept_stat__ 0.89 1.8e-03 0.12 0.63 0.94 1.0 4996 17168 1.0e+00
-#> stepsize__ 0.80 4.6e-02 0.066 0.73 0.85 0.88 2.0 6.9 2.6e+13
-#> treedepth__ 2.3 8.8e-02 0.50 2.0 2.0 3.0 32 109 1.0e+00
-#> n_leapfrog__ 4.8 2.8e-01 2.0 3.0 3.0 7.0 53 182 1.0e+00
+#> lp__ -6.6e+01 3.1e-02 1.4 -69 -6.6e+01 -6.4e+01 2120 7545 1.0
+#> accept_stat__ 0.91 1.4e-03 0.10 0.69 0.95 1.0 5437 19350 1.0e+00
+#> stepsize__ 0.74 2.1e-02 0.030 0.70 0.75 0.78 2.0 7.1 1.1e+13
+#> treedepth__ 2.4 8.8e-03 0.53 2.0 2.0 3.0 3601 12815 1.0e+00
+#> n_leapfrog__ 5.3 3.3e-02 2.0 3.0 7.0 7.0 3773 13428 1.0e+00
#> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 nan nan nan
-#> energy__ 68 4.5e-02 2.0 65 68 72 1941 6671 1.0e+00
+#> energy__ 68 4.9e-02 2.0 65 68 72 1750 6227 1.0e+00
#>
-#> alpha 3.8e-01 3.5e-03 0.22 0.014 3.8e-01 7.4e-01 4054 13931 1.0
-#> beta[1] -6.7e-01 3.7e-03 0.25 -1.1 -6.7e-01 -2.8e-01 4566 15692 1.0
-#> beta[2] -2.8e-01 3.9e-03 0.23 -0.66 -2.8e-01 9.5e-02 3521 12100 1.0
-#> beta[3] 6.8e-01 4.0e-03 0.26 0.25 6.8e-01 1.1e+00 4334 14894 1.0
-#> log_lik[1] -5.2e-01 1.6e-03 0.10 -0.69 -5.1e-01 -3.6e-01 4009 13775 1.0
-#> log_lik[2] -4.0e-01 2.2e-03 0.15 -0.69 -3.8e-01 -1.9e-01 4671 16052 1.0
-#> log_lik[3] -5.0e-01 3.7e-03 0.22 -0.91 -4.7e-01 -2.1e-01 3570 12267 1.0
-#> log_lik[4] -4.5e-01 2.4e-03 0.15 -0.72 -4.3e-01 -2.3e-01 3825 13143 1.0
-#> log_lik[5] -1.2e+00 4.1e-03 0.28 -1.7 -1.2e+00 -7.6e-01 4632 15916 1.0
-#> log_lik[6] -5.9e-01 2.8e-03 0.19 -0.93 -5.7e-01 -3.2e-01 4602 15813 1.0
-#> log_lik[7] -6.4e-01 1.9e-03 0.13 -0.86 -6.3e-01 -4.5e-01 4425 15206 1.0
-#> log_lik[8] -2.7e-01 2.3e-03 0.14 -0.54 -2.5e-01 -1.0e-01 3551 12204 1.0
-#> log_lik[9] -6.9e-01 2.6e-03 0.17 -0.99 -6.8e-01 -4.4e-01 4396 15105 1.0
-#> log_lik[10] -7.5e-01 4.0e-03 0.24 -1.2 -7.2e-01 -4.1e-01 3524 12111 1.0
-#> log_lik[11] -2.8e-01 1.9e-03 0.12 -0.51 -2.6e-01 -1.2e-01 4067 13978 1.00
-#> log_lik[12] -5.0e-01 3.5e-03 0.24 -0.94 -4.6e-01 -1.9e-01 4672 16056 1.00
-#> log_lik[13] -6.6e-01 3.0e-03 0.21 -1.0 -6.4e-01 -3.6e-01 4681 16086 1.0
-#> log_lik[14] -3.6e-01 2.9e-03 0.17 -0.69 -3.3e-01 -1.4e-01 3667 12602 1.0
-#> log_lik[15] -2.8e-01 1.6e-03 0.11 -0.47 -2.6e-01 -1.4e-01 4253 14617 1.0
-#> log_lik[16] -2.7e-01 1.5e-03 0.087 -0.43 -2.6e-01 -1.5e-01 3572 12276 1.0
-#> log_lik[17] -1.6e+00 4.6e-03 0.29 -2.1 -1.6e+00 -1.2e+00 3940 13539 1.00
-#> log_lik[18] -4.8e-01 1.7e-03 0.11 -0.68 -4.7e-01 -3.1e-01 4208 14461 1.0
-#> log_lik[19] -2.3e-01 1.3e-03 0.075 -0.37 -2.2e-01 -1.2e-01 3488 11985 1.00
-#> log_lik[20] -1.1e-01 1.3e-03 0.078 -0.26 -9.3e-02 -2.8e-02 3400 11685 1.0
-#> log_lik[21] -2.1e-01 1.4e-03 0.086 -0.37 -2.0e-01 -9.3e-02 3747 12875 1.0
-#> log_lik[22] -5.7e-01 2.3e-03 0.15 -0.84 -5.5e-01 -3.5e-01 4159 14294 1.0
-#> log_lik[23] -3.3e-01 2.3e-03 0.14 -0.60 -3.0e-01 -1.4e-01 3876 13319 1.0
-#> log_lik[24] -1.3e-01 1.1e-03 0.066 -0.26 -1.2e-01 -5.1e-02 3311 11379 1.0
-#> log_lik[25] -4.5e-01 1.9e-03 0.12 -0.67 -4.4e-01 -2.7e-01 4263 14650 1.0
-#> log_lik[26] -1.5e+00 4.9e-03 0.34 -2.1 -1.5e+00 -1.0e+00 4863 16712 1.00
-#> log_lik[27] -3.0e-01 1.9e-03 0.12 -0.53 -2.9e-01 -1.4e-01 3942 13545 1.0
-#> log_lik[28] -4.4e-01 1.3e-03 0.085 -0.59 -4.4e-01 -3.1e-01 4011 13785 1.0
-#> log_lik[29] -7.2e-01 3.4e-03 0.23 -1.1 -6.9e-01 -3.9e-01 4401 15125 1.0
-#> log_lik[30] -6.9e-01 2.9e-03 0.20 -1.0 -6.7e-01 -4.0e-01 4483 15406 1.0
-#> log_lik[31] -4.9e-01 2.5e-03 0.16 -0.78 -4.7e-01 -2.6e-01 4140 14226 1.0
-#> log_lik[32] -4.2e-01 1.6e-03 0.11 -0.61 -4.1e-01 -2.7e-01 4397 15110 1.0
-#> log_lik[33] -4.1e-01 1.9e-03 0.13 -0.64 -4.0e-01 -2.2e-01 4495 15446 1.0
-#> log_lik[34] -6.2e-02 8.6e-04 0.050 -0.16 -4.8e-02 -1.2e-02 3377 11605 1.0
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+#> log_lik[84] -2.7e-01 1.5e-03 0.090 -0.44 -2.6e-01 -1.4e-01 3767 13405 1.00
+#> log_lik[85] -1.3e-01 1.2e-03 0.075 -0.28 -1.1e-01 -4.1e-02 3780 13453 1.00
+#> log_lik[86] -1.1e+00 5.0e-03 0.32 -1.7 -1.1e+00 -6.4e-01 4173 14850 1.00
+#> log_lik[87] -8.3e-01 2.0e-03 0.13 -1.1 -8.2e-01 -6.2e-01 4183 14887 1.00
+#> log_lik[88] -7.8e-01 3.8e-03 0.24 -1.2 -7.5e-01 -4.2e-01 4157 14794 1.0
+#> log_lik[89] -1.3e+00 5.1e-03 0.32 -1.9 -1.2e+00 -7.9e-01 3879 13805 1.00
+#> log_lik[90] -2.6e-01 2.2e-03 0.14 -0.52 -2.4e-01 -9.4e-02 4008 14262 1.0
+#> log_lik[91] -3.9e-01 2.1e-03 0.13 -0.63 -3.8e-01 -2.1e-01 4144 14746 1.0
+#> log_lik[92] -1.5e+00 5.6e-03 0.34 -2.1 -1.5e+00 -9.7e-01 3829 13625 1.00
+#> log_lik[93] -7.5e-01 3.4e-03 0.21 -1.1 -7.2e-01 -4.4e-01 3888 13835 1.00
+#> log_lik[94] -3.2e-01 1.4e-03 0.087 -0.47 -3.1e-01 -1.9e-01 3803 13533 1.00
+#> log_lik[95] -3.9e-01 1.9e-03 0.12 -0.60 -3.8e-01 -2.2e-01 3889 13841 1.0
+#> log_lik[96] -1.6e+00 4.8e-03 0.29 -2.1 -1.6e+00 -1.1e+00 3601 12815 1.00
+#> log_lik[97] -4.3e-01 1.6e-03 0.10 -0.61 -4.2e-01 -2.7e-01 4067 14472 1.0
+#> log_lik[98] -1.0e+00 5.6e-03 0.37 -1.7 -1.0e+00 -5.2e-01 4342 15452 1.0
+#> log_lik[99] -7.0e-01 2.2e-03 0.15 -0.96 -6.9e-01 -4.7e-01 4351 15484 1.00
+#> log_lik[100] -3.9e-01 1.5e-03 0.097 -0.55 -3.8e-01 -2.4e-01 4019 14301 1.00
#>
#> Samples were drawn using hmc with nuts.
#> For each parameter, N_Eff is a crude measure of effective sample size,
diff --git a/docs/reference/fit-method-diagnostic_summary.html b/docs/reference/fit-method-diagnostic_summary.html
index 97dce2c2..53c45c1c 100644
--- a/docs/reference/fit-method-diagnostic_summary.html
+++ b/docs/reference/fit-method-diagnostic_summary.html
@@ -161,34 +161,29 @@ See also
Examples
# \dontrun{
fit <- cmdstanr_example("schools")
-#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
-#> Chain 2 Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model-11d99572dc9ce.stan', line 14, column 2 to column 41)
-#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
-#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
-#> Chain 2
-#> Warning: 103 of 4000 (3.0%) transitions ended with a divergence.
+#> Warning: 91 of 4000 (2.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.
fit$diagnostic_summary()
-#> Warning: 103 of 4000 (3.0%) transitions ended with a divergence.
+#> Warning: 91 of 4000 (2.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.
#> $num_divergent
-#> [1] 2 40 38 23
+#> [1] 12 18 11 50
#>
#> $num_max_treedepth
#> [1] 0 0 0 0
#>
#> $ebfmi
-#> [1] 0.2865692 0.3867145 0.2763028 0.3720129
+#> [1] 0.3003970 0.2974182 0.3438194 0.4920025
#>
fit$diagnostic_summary(quiet = TRUE)
#> $num_divergent
-#> [1] 2 40 38 23
+#> [1] 12 18 11 50
#>
#> $num_max_treedepth
#> [1] 0 0 0 0
#>
#> $ebfmi
-#> [1] 0.2865692 0.3867145 0.2763028 0.3720129
+#> [1] 0.3003970 0.2974182 0.3438194 0.4920025
#>
# }
diff --git a/docs/reference/fit-method-draws-1.png b/docs/reference/fit-method-draws-1.png
index 27dbcc21..a6899fc4 100644
Binary files a/docs/reference/fit-method-draws-1.png and b/docs/reference/fit-method-draws-1.png differ
diff --git a/docs/reference/fit-method-draws-2.png b/docs/reference/fit-method-draws-2.png
index 78e9ff03..fb93238c 100644
Binary files a/docs/reference/fit-method-draws-2.png and b/docs/reference/fit-method-draws-2.png differ
diff --git a/docs/reference/fit-method-draws.html b/docs/reference/fit-method-draws.html
index 1a2ac8f7..b257cb5d 100644
--- a/docs/reference/fit-method-draws.html
+++ b/docs/reference/fit-method-draws.html
@@ -203,7 +203,7 @@ Examples
dim(draws)
#> [1] 1000 4 105
str(draws)
-#> 'draws_array' num [1:1000, 1:4, 1:105] -65.8 -67 -66.3 -66.2 -66.8 ...
+#> 'draws_array' num [1:1000, 1:4, 1:105] -64.2 -65.3 -65.3 -64.7 -64.8 ...
#> - attr(*, "dimnames")=List of 3
#> ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
#> ..$ chain : chr [1:4] "1" "2" "3" "4"
@@ -215,12 +215,12 @@ Examples
#> # A draws_matrix: 6 iterations, 1 chains, and 105 variables
#> variable
#> draw lp__ alpha beta[1] beta[2] beta[3] log_lik[1] log_lik[2] log_lik[3]
-#> 1 -66 0.24 -0.55 0.09 0.39 -0.52 -0.42 -0.32
-#> 2 -67 0.52 -0.85 -0.20 1.28 -0.46 -0.18 -0.26
-#> 3 -66 0.19 -0.80 -0.31 0.20 -0.54 -0.53 -0.62
-#> 4 -66 0.39 -0.57 -0.68 1.03 -0.59 -0.33 -0.81
-#> 5 -67 0.46 -0.49 -0.63 1.16 -0.57 -0.30 -0.72
-#> 6 -65 0.30 -0.44 -0.21 0.28 -0.54 -0.59 -0.56
+#> 1 -64 0.28 -0.73 -0.16 0.73 -0.53 -0.30 -0.39
+#> 2 -65 0.19 -0.42 -0.13 0.37 -0.59 -0.48 -0.53
+#> 3 -65 0.61 -0.93 -0.25 0.83 -0.40 -0.34 -0.30
+#> 4 -65 0.42 -0.81 -0.32 0.94 -0.50 -0.28 -0.41
+#> 5 -65 0.36 -0.41 -0.20 0.39 -0.52 -0.56 -0.52
+#> 6 -67 0.55 -0.24 -0.43 0.24 -0.49 -0.87 -0.75
#> # ... with 97 more variables
# or can specify 'format' argument to avoid manual conversion
@@ -230,12 +230,12 @@ Examples
#> # A draws_matrix: 6 iterations, 1 chains, and 105 variables
#> variable
#> draw lp__ alpha beta[1] beta[2] beta[3] log_lik[1] log_lik[2] log_lik[3]
-#> 1 -66 0.24 -0.55 0.09 0.39 -0.52 -0.42 -0.32
-#> 2 -67 0.52 -0.85 -0.20 1.28 -0.46 -0.18 -0.26
-#> 3 -66 0.19 -0.80 -0.31 0.20 -0.54 -0.53 -0.62
-#> 4 -66 0.39 -0.57 -0.68 1.03 -0.59 -0.33 -0.81
-#> 5 -67 0.46 -0.49 -0.63 1.16 -0.57 -0.30 -0.72
-#> 6 -65 0.30 -0.44 -0.21 0.28 -0.54 -0.59 -0.56
+#> 1 -64 0.28 -0.73 -0.16 0.73 -0.53 -0.30 -0.39
+#> 2 -65 0.19 -0.42 -0.13 0.37 -0.59 -0.48 -0.53
+#> 3 -65 0.61 -0.93 -0.25 0.83 -0.40 -0.34 -0.30
+#> 4 -65 0.42 -0.81 -0.32 0.94 -0.50 -0.28 -0.41
+#> 5 -65 0.36 -0.41 -0.20 0.39 -0.52 -0.56 -0.52
+#> 6 -67 0.55 -0.24 -0.43 0.24 -0.49 -0.87 -0.75
#> # ... with 97 more variables
# can select specific parameters
@@ -244,12 +244,12 @@ Examples
#> , , variable = alpha
#>
#> chain
-#> iteration 1 2 3 4
-#> 1 0.24 0.310 0.180 0.67
-#> 2 0.52 -0.014 0.351 0.23
-#> 3 0.19 0.366 0.682 0.48
-#> 4 0.39 0.645 0.098 0.49
-#> 5 0.46 0.177 0.677 0.65
+#> iteration 1 2 3 4
+#> 1 0.28 0.27 0.62 0.10
+#> 2 0.19 0.70 0.49 0.71
+#> 3 0.61 0.26 0.89 0.14
+#> 4 0.42 0.39 0.58 0.39
+#> 5 0.36 0.39 0.58 0.48
#>
#> # ... with 995 more iterations
fit$draws("beta") # selects entire vector beta
@@ -258,31 +258,31 @@ Examples
#>
#> chain
#> iteration 1 2 3 4
-#> 1 -0.55 -1.00 -0.41 -0.55
-#> 2 -0.85 -0.45 -0.62 -0.91
-#> 3 -0.80 -0.52 -0.86 -0.50
-#> 4 -0.57 -0.46 -0.50 -0.57
-#> 5 -0.49 -0.97 -1.00 -0.76
+#> 1 -0.73 -0.46 -0.82 -0.55
+#> 2 -0.42 -0.92 -1.18 -1.08
+#> 3 -0.93 -0.52 -0.63 -0.42
+#> 4 -0.81 -0.86 -0.30 -0.72
+#> 5 -0.41 -0.86 -0.30 -0.86
#>
#> , , variable = beta[2]
#>
#> chain
-#> iteration 1 2 3 4
-#> 1 0.09 -0.32 -0.132 -0.091
-#> 2 -0.20 -0.45 -0.283 -0.479
-#> 3 -0.31 -0.25 -0.335 -0.681
-#> 4 -0.68 -0.25 0.093 -0.717
-#> 5 -0.63 -0.23 -0.227 -0.328
+#> iteration 1 2 3 4
+#> 1 -0.16 -0.31 -0.88 -0.18
+#> 2 -0.13 -0.27 -0.69 -0.21
+#> 3 -0.25 -0.39 -0.38 -0.37
+#> 4 -0.32 -0.43 -0.52 -0.36
+#> 5 -0.20 -0.43 -0.52 -0.42
#>
#> , , variable = beta[3]
#>
#> chain
#> iteration 1 2 3 4
-#> 1 0.39 0.47 0.54 0.70
-#> 2 1.28 0.68 0.76 0.83
-#> 3 0.20 0.45 0.66 0.30
-#> 4 1.03 0.30 0.53 0.54
-#> 5 1.16 1.17 0.46 1.29
+#> 1 0.73 0.28 0.87 0.93
+#> 2 0.37 1.26 0.75 0.45
+#> 3 0.83 0.28 0.88 0.83
+#> 4 0.94 0.64 0.71 0.48
+#> 5 0.39 0.64 0.71 0.41
#>
#> # ... with 995 more iterations
fit$draws(c("alpha", "beta[2]"))
@@ -290,22 +290,22 @@ Examples
#> , , variable = alpha
#>
#> chain
-#> iteration 1 2 3 4
-#> 1 0.24 0.310 0.180 0.67
-#> 2 0.52 -0.014 0.351 0.23
-#> 3 0.19 0.366 0.682 0.48
-#> 4 0.39 0.645 0.098 0.49
-#> 5 0.46 0.177 0.677 0.65
+#> iteration 1 2 3 4
+#> 1 0.28 0.27 0.62 0.10
+#> 2 0.19 0.70 0.49 0.71
+#> 3 0.61 0.26 0.89 0.14
+#> 4 0.42 0.39 0.58 0.39
+#> 5 0.36 0.39 0.58 0.48
#>
#> , , variable = beta[2]
#>
#> chain
-#> iteration 1 2 3 4
-#> 1 0.09 -0.32 -0.132 -0.091
-#> 2 -0.20 -0.45 -0.283 -0.479
-#> 3 -0.31 -0.25 -0.335 -0.681
-#> 4 -0.68 -0.25 0.093 -0.717
-#> 5 -0.63 -0.23 -0.227 -0.328
+#> iteration 1 2 3 4
+#> 1 -0.16 -0.31 -0.88 -0.18
+#> 2 -0.13 -0.27 -0.69 -0.21
+#> 3 -0.25 -0.39 -0.38 -0.37
+#> 4 -0.32 -0.43 -0.52 -0.36
+#> 5 -0.20 -0.43 -0.52 -0.42
#>
#> # ... with 995 more iterations
@@ -323,21 +323,21 @@ Examples
#> # A draws_matrix: 6 iterations, 1 chains, and 3 variables
#> variable
#> draw beta[1] beta[2] beta[3]
-#> 1 -0.74 0.09 0.55
-#> 2 -0.76 0.39 0.68
-#> 3 -0.43 -0.15 1.06
-#> 4 -1.15 -0.35 0.54
-#> 5 -0.60 -0.26 0.34
-#> 6 -0.30 -0.24 1.03
+#> 1 -0.62 -0.485 0.63
+#> 2 -0.34 0.188 0.77
+#> 3 -0.63 -0.236 0.90
+#> 4 -0.65 -0.392 0.76
+#> 5 -0.57 0.028 0.44
+#> 6 -0.58 -0.299 0.69
head(fit$draws("beta", format = "df"))
#> # A draws_df: 6 iterations, 1 chains, and 3 variables
#> beta[1] beta[2] beta[3]
-#> 1 -0.74 0.09 0.55
-#> 2 -0.76 0.39 0.68
-#> 3 -0.43 -0.15 1.06
-#> 4 -1.15 -0.35 0.54
-#> 5 -0.60 -0.26 0.34
-#> 6 -0.30 -0.24 1.03
+#> 1 -0.62 -0.485 0.63
+#> 2 -0.34 0.188 0.77
+#> 3 -0.63 -0.236 0.90
+#> 4 -0.65 -0.392 0.76
+#> 5 -0.57 0.028 0.44
+#> 6 -0.58 -0.299 0.69
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
# }
diff --git a/docs/reference/fit-method-grad_log_prob.html b/docs/reference/fit-method-grad_log_prob.html
index 79de7ede..e6dae257 100644
--- a/docs/reference/fit-method-grad_log_prob.html
+++ b/docs/reference/fit-method-grad_log_prob.html
@@ -113,19 +113,26 @@ Calculate the log-probability and the gradient w.r.t. each input for a
- grad_log_prob(unconstrained_variables, jacobian_adjustment = TRUE)
+ grad_log_prob(
+ unconstrained_variables,
+ jacobian = TRUE,
+ jacobian_adjustment = NULL
+)
Arguments
- unconstrained_variables
-(numeric) A vector of unconstrained parameters
-to be passed to grad_log_prob.
(numeric) A vector of unconstrained parameters.
- jacobian
+(logical) Whether to include the log-density adjustments from
+un/constraining variables.
- jacobian_adjustment
-(logical) Whether to include the log-density
-adjustments from un/constraining variables.
Deprecated. Please use jacobian instead.
diff --git a/docs/reference/fit-method-gradients.html b/docs/reference/fit-method-gradients.html
index 828d0688..c682dd04 100644
--- a/docs/reference/fit-method-gradients.html
+++ b/docs/reference/fit-method-gradients.html
@@ -131,11 +131,11 @@ Examples
# retrieve the gradients
test$gradients()
-#> param_idx value model finite_diff error
-#> 1 0 1.376210 -19.10810 -19.10810 5.51201e-09
-#> 2 1 -0.796954 -2.04336 -2.04336 5.51900e-09
-#> 3 2 0.520069 -11.77830 -11.77830 -6.60224e-10
-#> 4 3 0.122833 7.41794 7.41794 4.62926e-08
+#> param_idx value model finite_diff error
+#> 1 0 1.083910 -6.21416 -6.21416 3.58858e-08
+#> 2 1 1.800640 -33.18970 -33.18970 4.38607e-08
+#> 3 2 0.683175 -19.70780 -19.70780 1.73640e-08
+#> 4 3 1.857330 -9.17992 -9.17992 1.12563e-08
# }
diff --git a/docs/reference/fit-method-hessian.html b/docs/reference/fit-method-hessian.html
index b08bb4ea..12bcc5e8 100644
--- a/docs/reference/fit-method-hessian.html
+++ b/docs/reference/fit-method-hessian.html
@@ -113,19 +113,22 @@ Calculate the log-probability , the gradient w.r.t. each input, and the hess
- hessian(unconstrained_variables, jacobian_adjustment = TRUE)
+ hessian(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = TRUE)
Arguments
- unconstrained_variables
-(numeric) A vector of unconstrained parameters
-to be passed to hessian.
(numeric) A vector of unconstrained parameters.
- jacobian
+(logical) Whether to include the log-density adjustments from
+un/constraining variables.
- jacobian_adjustment
-(logical) Whether to include the log-density
-adjustments from un/constraining variables.
Deprecated. Please use jacobian instead.
diff --git a/docs/reference/fit-method-init.html b/docs/reference/fit-method-init.html
index e6001ccc..955ed4b1 100644
--- a/docs/reference/fit-method-init.html
+++ b/docs/reference/fit-method-init.html
@@ -140,11 +140,11 @@ Examples
str(fit$init())
#> List of 2
#> $ :List of 2
-#> ..$ alpha: num 0.865
-#> ..$ beta : num [1:3] -0.0306 -0.1349 1.1272
+#> ..$ alpha: num -0.538
+#> ..$ beta : num [1:3] -0.0289 1.8267 1.6042
#> $ :List of 2
-#> ..$ alpha: num -1.41
-#> ..$ beta : num [1:3] 0.143 -1.043 0.9
+#> ..$ alpha: num -1.4
+#> ..$ beta : num [1:3] -1.313 -1.04 0.224
# partial inits (only specifying for a subset of parameters)
init_list <- list(
diff --git a/docs/reference/fit-method-inv_metric.html b/docs/reference/fit-method-inv_metric.html
index feac9970..3705aecd 100644
--- a/docs/reference/fit-method-inv_metric.html
+++ b/docs/reference/fit-method-inv_metric.html
@@ -140,75 +140,75 @@ Examples
fit$inv_metric()
#> $`1`
#> [,1] [,2] [,3] [,4]
-#> [1,] 0.0461164 0.0000000 0.0000000 0.0000000
-#> [2,] 0.0000000 0.0586249 0.0000000 0.0000000
-#> [3,] 0.0000000 0.0000000 0.0569083 0.0000000
-#> [4,] 0.0000000 0.0000000 0.0000000 0.0695349
+#> [1,] 0.0435508 0.0000000 0.0000000 0.0000000
+#> [2,] 0.0000000 0.0607469 0.0000000 0.0000000
+#> [3,] 0.0000000 0.0000000 0.0503494 0.0000000
+#> [4,] 0.0000000 0.0000000 0.0000000 0.0672705
#>
#> $`2`
#> [,1] [,2] [,3] [,4]
-#> [1,] 0.0423827 0.0000000 0.0000000 0.0000000
-#> [2,] 0.0000000 0.0638639 0.0000000 0.0000000
-#> [3,] 0.0000000 0.0000000 0.0463094 0.0000000
-#> [4,] 0.0000000 0.0000000 0.0000000 0.0646605
+#> [1,] 0.0532065 0.0000000 0.0000000 0.0000000
+#> [2,] 0.0000000 0.0574785 0.0000000 0.0000000
+#> [3,] 0.0000000 0.0000000 0.0512835 0.0000000
+#> [4,] 0.0000000 0.0000000 0.0000000 0.0641874
#>
#> $`3`
#> [,1] [,2] [,3] [,4]
-#> [1,] 0.0555959 0.0000000 0.0000000 0.0000000
-#> [2,] 0.0000000 0.0595803 0.0000000 0.0000000
-#> [3,] 0.0000000 0.0000000 0.0515264 0.0000000
-#> [4,] 0.0000000 0.0000000 0.0000000 0.0759908
+#> [1,] 0.0502858 0.0000000 0.0000000 0.0000000
+#> [2,] 0.0000000 0.0629888 0.0000000 0.0000000
+#> [3,] 0.0000000 0.0000000 0.0512933 0.0000000
+#> [4,] 0.0000000 0.0000000 0.0000000 0.0661664
#>
#> $`4`
-#> [,1] [,2] [,3] [,4]
-#> [1,] 0.05104 0.000000 0.0000000 0.00000
-#> [2,] 0.00000 0.051252 0.0000000 0.00000
-#> [3,] 0.00000 0.000000 0.0567318 0.00000
-#> [4,] 0.00000 0.000000 0.0000000 0.06699
+#> [,1] [,2] [,3] [,4]
+#> [1,] 0.0453724 0.000000 0.00000 0.0000000
+#> [2,] 0.0000000 0.061371 0.00000 0.0000000
+#> [3,] 0.0000000 0.000000 0.04017 0.0000000
+#> [4,] 0.0000000 0.000000 0.00000 0.0682251
#>
fit$inv_metric(matrix=FALSE)
#> $`1`
-#> [1] 0.0461164 0.0586249 0.0569083 0.0695349
+#> [1] 0.0435508 0.0607469 0.0503494 0.0672705
#>
#> $`2`
-#> [1] 0.0423827 0.0638639 0.0463094 0.0646605
+#> [1] 0.0532065 0.0574785 0.0512835 0.0641874
#>
#> $`3`
-#> [1] 0.0555959 0.0595803 0.0515264 0.0759908
+#> [1] 0.0502858 0.0629888 0.0512933 0.0661664
#>
#> $`4`
-#> [1] 0.0510400 0.0512520 0.0567318 0.0669900
+#> [1] 0.0453724 0.0613710 0.0401700 0.0682251
#>
fit <- cmdstanr_example("logistic", metric = "dense_e")
fit$inv_metric()
#> $`1`
-#> [,1] [,2] [,3] [,4]
-#> [1,] 0.048223400 -0.00737038 0.000297676 0.00680596
-#> [2,] -0.007370380 0.05440320 -0.006681250 -0.00162053
-#> [3,] 0.000297676 -0.00668125 0.043576600 -0.00859282
-#> [4,] 0.006805960 -0.00162053 -0.008592820 0.06856220
+#> [,1] [,2] [,3] [,4]
+#> [1,] 0.04487240 -0.00665557 0.00248036 -0.00336473
+#> [2,] -0.00665557 0.05394230 -0.00841737 -0.00815855
+#> [3,] 0.00248036 -0.00841737 0.04945140 -0.01015960
+#> [4,] -0.00336473 -0.00815855 -0.01015960 0.07289150
#>
#> $`2`
-#> [,1] [,2] [,3] [,4]
-#> [1,] 0.04746980 -0.00175084 -0.00118626 0.00664285
-#> [2,] -0.00175084 0.06320920 -0.00172180 -0.01259270
-#> [3,] -0.00118626 -0.00172180 0.04810590 -0.00872089
-#> [4,] 0.00664285 -0.01259270 -0.00872089 0.06689500
+#> [,1] [,2] [,3] [,4]
+#> [1,] 0.044891100 -0.00756532 0.00352846 -0.000462971
+#> [2,] -0.007565320 0.06064960 -0.00809279 -0.004813840
+#> [3,] 0.003528460 -0.00809279 0.05892730 -0.016947100
+#> [4,] -0.000462971 -0.00481384 -0.01694710 0.063878300
#>
#> $`3`
-#> [,1] [,2] [,3] [,4]
-#> [1,] 0.04482880 -0.00377658 -0.00343049 0.00304829
-#> [2,] -0.00377658 0.04803130 -0.00957526 -0.00566723
-#> [3,] -0.00343049 -0.00957526 0.05099840 -0.00574551
-#> [4,] 0.00304829 -0.00566723 -0.00574551 0.07092750
+#> [,1] [,2] [,3] [,4]
+#> [1,] 0.047806100 0.000761202 0.00579535 0.00494546
+#> [2,] 0.000761202 0.062074500 -0.00488086 -0.01014510
+#> [3,] 0.005795350 -0.004880860 0.05170230 -0.00609765
+#> [4,] 0.004945460 -0.010145100 -0.00609765 0.07081120
#>
#> $`4`
-#> [,1] [,2] [,3] [,4]
-#> [1,] 0.04787540 -0.003918010 -0.004273680 0.00253868
-#> [2,] -0.00391801 0.049792000 0.000712128 -0.00453177
-#> [3,] -0.00427368 0.000712128 0.045850800 -0.00639672
-#> [4,] 0.00253868 -0.004531770 -0.006396720 0.07511110
+#> [,1] [,2] [,3] [,4]
+#> [1,] 0.04387350 0.004639300 0.001929550 0.00526756
+#> [2,] 0.00463930 0.064076200 -0.000902685 -0.00609741
+#> [3,] 0.00192955 -0.000902685 0.045911200 -0.00859578
+#> [4,] 0.00526756 -0.006097410 -0.008595780 0.07087270
#>
# }
diff --git a/docs/reference/fit-method-log_prob.html b/docs/reference/fit-method-log_prob.html
index a0a9f638..561ca052 100644
--- a/docs/reference/fit-method-log_prob.html
+++ b/docs/reference/fit-method-log_prob.html
@@ -1,5 +1,6 @@
-Calculate the log-probability given a provided vector of unconstrained parameters. — fit-method-log_prob • cmdstanr Calculate the log-probability given a provided vector of unconstrained parameters. — fit-method-log_prob • cmdstanr
@@ -104,23 +105,27 @@ Calculate the log-probability given a provided vector of unconstrained param
- The $log_prob() method provides access to the Stan model's log_prob function
+ The $log_prob() method provides access to the Stan model's
+log_prob function.
- log_prob(unconstrained_variables, jacobian_adjustment = TRUE)
+ log_prob(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = NULL)
Arguments
- unconstrained_variables
-(numeric) A vector of unconstrained parameters
-to be passed to log_prob.
(numeric) A vector of unconstrained parameters.
- jacobian
+(logical) Whether to include the log-density adjustments from
+un/constraining variables.
- jacobian_adjustment
-(logical) Whether to include the log-density
-adjustments from un/constraining variables.
Deprecated. Please use jacobian instead.
diff --git a/docs/reference/fit-method-loo.html b/docs/reference/fit-method-loo.html
index 5a1a1743..54c58466 100644
--- a/docs/reference/fit-method-loo.html
+++ b/docs/reference/fit-method-loo.html
@@ -181,9 +181,9 @@ Examples
#> Computed from 4000 by 100 log-likelihood matrix
#>
#> Estimate SE
-#> elpd_loo -63.7 4.2
+#> elpd_loo -63.7 4.1
#> p_loo 3.9 0.5
-#> looic 127.3 8.3
+#> looic 127.4 8.2
#> ------
#> Monte Carlo SE of elpd_loo is 0.0.
#>
diff --git a/docs/reference/fit-method-lp-1.png b/docs/reference/fit-method-lp-1.png
index 508c1b84..980bd46b 100644
Binary files a/docs/reference/fit-method-lp-1.png and b/docs/reference/fit-method-lp-1.png differ
diff --git a/docs/reference/fit-method-lp.html b/docs/reference/fit-method-lp.html
index 1dd19bc7..bca9a329 100644
--- a/docs/reference/fit-method-lp.html
+++ b/docs/reference/fit-method-lp.html
@@ -170,7 +170,7 @@ Examples
# \dontrun{
fit_mcmc <- cmdstanr_example("logistic")
head(fit_mcmc$lp())
-#> [1] -65.4484 -67.4196 -65.8307 -65.3367 -67.3193 -66.8288
+#> [1] -65.6405 -64.4734 -65.4431 -64.9703 -65.8088 -65.6905
fit_mle <- cmdstanr_example("logistic", method = "optimize")
fit_mle$lp()
diff --git a/docs/reference/fit-method-metadata.html b/docs/reference/fit-method-metadata.html
index f97d066f..01634904 100644
--- a/docs/reference/fit-method-metadata.html
+++ b/docs/reference/fit-method-metadata.html
@@ -129,7 +129,7 @@ Examples
#> $ stan_version_major : num 2
#> $ stan_version_minor : num 33
#> $ stan_version_patch : num 0
-#> $ start_datetime : chr "2023-11-08 17:29:44 UTC"
+#> $ start_datetime : chr "2023-12-13 17:04:42 UTC"
#> $ method : chr "sample"
#> $ save_warmup : num 0
#> $ thin : num 1
@@ -146,14 +146,14 @@ Examples
#> $ num_chains : num 1
#> $ id : num [1:4] 1 2 3 4
#> $ init : num [1:4] 2 2 2 2
-#> $ seed : num 1.39e+09
+#> $ seed : num 1.08e+09
#> $ refresh : num 100
#> $ sig_figs : num -1
-#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-216cf7.csv"
+#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-79f552.csv"
#> $ stanc_version : chr "stanc3 v2.33.1"
#> $ sampler_diagnostics : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ...
#> $ variables : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
-#> $ step_size_adaptation: num [1:4] 0.779 0.737 0.698 0.697
+#> $ step_size_adaptation: num [1:4] 0.723 0.882 0.802 0.749
#> $ model_name : chr "logistic_model"
#> $ adapt_engaged : num 1
#> $ adapt_delta : num 0.8
@@ -164,9 +164,9 @@ Examples
#> $ threads_per_chain : num 1
#> $ time :'data.frame': 4 obs. of 4 variables:
#> ..$ chain_id: num [1:4] 1 2 3 4
-#> ..$ warmup : num [1:4] 0.022 0.023 0.023 0.023
-#> ..$ sampling: num [1:4] 0.081 0.074 0.083 0.081
-#> ..$ total : num [1:4] 0.103 0.097 0.106 0.104
+#> ..$ warmup : num [1:4] 0.022 0.022 0.022 0.022
+#> ..$ sampling: num [1:4] 0.069 0.065 0.07 0.068
+#> ..$ total : num [1:4] 0.091 0.087 0.092 0.09
#> $ stan_variable_sizes :List of 4
#> ..$ lp__ : num 1
#> ..$ alpha : num 1
@@ -181,7 +181,7 @@ Examples
#> $ stan_version_major : num 2
#> $ stan_version_minor : num 33
#> $ stan_version_patch : num 0
-#> $ start_datetime : chr "2023-11-08 17:29:46 UTC"
+#> $ start_datetime : chr "2023-12-13 17:04:44 UTC"
#> $ method : chr "optimize"
#> $ algorithm : chr "lbfgs"
#> $ init_alpha : num 0.001
@@ -196,10 +196,10 @@ Examples
#> $ save_iterations : num 0
#> $ id : num 1
#> $ init : num 2
-#> $ seed : num 1.28e+09
+#> $ seed : num 9.12e+08
#> $ refresh : num 100
#> $ sig_figs : num -1
-#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-65f6b8.csv"
+#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-7b3d9a.csv"
#> $ stanc_version : chr "stanc3 v2.33.1"
#> $ sampler_diagnostics: chr(0)
#> $ variables : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
@@ -220,7 +220,7 @@ Examples
#> $ stan_version_major : num 2
#> $ stan_version_minor : num 33
#> $ stan_version_patch : num 0
-#> $ start_datetime : chr "2023-11-08 17:29:46 UTC"
+#> $ start_datetime : chr "2023-12-13 17:04:44 UTC"
#> $ method : chr "variational"
#> $ algorithm : chr "meanfield"
#> $ iter : num 50
@@ -232,10 +232,10 @@ Examples
#> $ output_samples : num 1000
#> $ id : num 1
#> $ init : num 2
-#> $ seed : num 9.18e+08
+#> $ seed : num 3.89e+08
#> $ refresh : num 100
#> $ sig_figs : num -1
-#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-40a7a2.csv"
+#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-645608.csv"
#> $ stanc_version : chr "stanc3 v2.33.1"
#> $ sampler_diagnostics: chr(0)
#> $ variables : chr [1:106] "lp__" "lp_approx__" "alpha" "beta[1]" ...
diff --git a/docs/reference/fit-method-mle.html b/docs/reference/fit-method-mle.html
index 53b88a5e..c9ac5821 100644
--- a/docs/reference/fit-method-mle.html
+++ b/docs/reference/fit-method-mle.html
@@ -147,14 +147,14 @@ Examples
# \dontrun{
fit <- cmdstanr_example("logistic", method = "optimize")
fit$mle("alpha")
-#> alpha
-#> 0.36447
+#> alpha
+#> 0.364472
fit$mle("beta")
#> beta[1] beta[2] beta[3]
-#> -0.631542 -0.258968 0.648496
+#> -0.631560 -0.258925 0.648500
fit$mle("beta[2]")
#> beta[2]
-#> -0.258968
+#> -0.258925
# }
diff --git a/docs/reference/fit-method-output.html b/docs/reference/fit-method-output.html
index d981dc43..ba2dc64f 100644
--- a/docs/reference/fit-method-output.html
+++ b/docs/reference/fit-method-output.html
@@ -166,21 +166,21 @@ Examples
#> num_chains = 1 (Default)
#> id = 1 (Default)
#> data
-#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpYtRZP3/temp_libpath119dc23a21f00/cmdstanr/logistic.data.json
+#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmps9TW4l/temp_libpath5800f91909/cmdstanr/logistic.data.json
#> init = 2 (Default)
#> random
-#> seed = 1225363303
+#> seed = 1467887685
#> output
-#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081029-1-2c7d41.csv
+#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131004-1-910e87.csv
#> diagnostic_file = (Default)
#> refresh = 100 (Default)
#> sig_figs = -1 (Default)
-#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-1ddd0e.csv
+#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-90a553.csv
#> num_threads = 1 (Default)
#>
#>
-#> Gradient evaluation took 2.2e-05 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
+#> Gradient evaluation took 1.9e-05 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Adjust your expectations accordingly!
#>
#>
@@ -207,9 +207,9 @@ Examples
#> Iteration: 1900 / 2000 [ 95%] (Sampling)
#> Iteration: 2000 / 2000 [100%] (Sampling)
#>
-#> Elapsed Time: 0.025 seconds (Warm-up)
-#> 0.082 seconds (Sampling)
-#> 0.107 seconds (Total)
+#> Elapsed Time: 0.022 seconds (Warm-up)
+#> 0.072 seconds (Sampling)
+#> 0.094 seconds (Total)
out <- fit_mcmc$output()
str(out)
#> List of 4
@@ -237,21 +237,21 @@ Examples
#> save_iterations = 0 (Default)
#> id = 1 (Default)
#> data
-#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpYtRZP3/temp_libpath119dc23a21f00/cmdstanr/logistic.data.json
+#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmps9TW4l/temp_libpath5800f91909/cmdstanr/logistic.data.json
#> init = 2 (Default)
#> random
-#> seed = 152179067
+#> seed = 44204223
#> output
-#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081029-1-0168cf.csv
+#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131004-1-75dd88.csv
#> diagnostic_file = (Default)
#> refresh = 100 (Default)
#> sig_figs = -1 (Default)
-#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-216c50.csv
+#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-5c2d53.csv
#> num_threads = 1 (Default)
#>
-#> Initial log joint probability = -95.9632
+#> Initial log joint probability = -80.1217
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 7 -63.9218 0.000242271 7.03951e-05 1 1 9
+#> 7 -63.9218 0.00150318 0.00093398 1 1 9
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
@@ -274,16 +274,16 @@ Examples
#> output_samples = 1000 (Default)
#> id = 1 (Default)
#> data
-#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpYtRZP3/temp_libpath119dc23a21f00/cmdstanr/logistic.data.json
+#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmps9TW4l/temp_libpath5800f91909/cmdstanr/logistic.data.json
#> init = 2 (Default)
#> random
-#> seed = 676659604
+#> seed = 244304498
#> output
-#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081029-1-6087e5.csv
+#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131004-1-3b4b11.csv
#> diagnostic_file = (Default)
#> refresh = 100 (Default)
#> sig_figs = -1 (Default)
-#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081029-1-8ae7e6.csv
+#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-85d291.csv
#> num_threads = 1 (Default)
#>
#> ------------------------------------------------------------
@@ -294,8 +294,8 @@ Examples
#>
#>
#>
-#> Gradient evaluation took 1.8e-05 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
+#> Gradient evaluation took 1.9e-05 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Adjust your expectations accordingly!
#>
#>
@@ -309,9 +309,9 @@ Examples
#>
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
-#> 100 -66.040 1.000 1.000
-#> 200 -66.445 0.503 1.000
-#> 300 -66.355 0.336 0.006 MEDIAN ELBO CONVERGED
+#> 100 -66.299 1.000 1.000
+#> 200 -66.336 0.500 1.000
+#> 300 -66.628 0.335 0.004 MEDIAN ELBO CONVERGED
#>
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
diff --git a/docs/reference/fit-method-profiles.html b/docs/reference/fit-method-profiles.html
index 6d97a13a..7c4e4b8c 100644
--- a/docs/reference/fit-method-profiles.html
+++ b/docs/reference/fit-method-profiles.html
@@ -176,32 +176,32 @@ Examples
fit$profiles()
#> [[1]]
#> name thread_id total_time forward_time reverse_time chain_stack
-#> 1 gq 0x1061d4e00 0.000364609 0.000364609 0.000000000 0
-#> 2 likelihood 0x1061d4e00 0.001171810 0.000840557 0.000331257 7169
+#> 1 gq 0x10efcde00 0.000375429 0.000375429 0.000000000 0
+#> 2 likelihood 0x10efcde00 0.001165040 0.000834157 0.000330887 7169
#> no_chain_stack autodiff_calls no_autodiff_calls
#> 1 0 0 1000
#> 2 7169 7169 1
#>
#> [[2]]
#> name thread_id total_time forward_time reverse_time chain_stack
-#> 1 gq 0x10e36de00 0.000346564 0.000346564 0.000000000 0
-#> 2 likelihood 0x10e36de00 0.001186840 0.000842553 0.000344283 7155
+#> 1 gq 0x10d0b0e00 0.000407951 0.000407951 0.000000000 0
+#> 2 likelihood 0x10d0b0e00 0.001283690 0.000915781 0.000367905 7155
#> no_chain_stack autodiff_calls no_autodiff_calls
#> 1 0 0 1000
#> 2 7155 7155 1
#>
#> [[3]]
#> name thread_id total_time forward_time reverse_time chain_stack
-#> 1 gq 0x111ab6e00 0.000351792 0.000351792 0.000000000 0
-#> 2 likelihood 0x111ab6e00 0.001143140 0.000829663 0.000313482 6879
+#> 1 gq 0x106bb1e00 0.000399315 0.000399315 0.000000000 0
+#> 2 likelihood 0x106bb1e00 0.001242900 0.000887379 0.000355522 6879
#> no_chain_stack autodiff_calls no_autodiff_calls
#> 1 0 0 1000
#> 2 6879 6879 1
#>
#> [[4]]
#> name thread_id total_time forward_time reverse_time chain_stack
-#> 1 gq 0x10c867e00 0.000408638 0.000408638 0.000000000 0
-#> 2 likelihood 0x10c867e00 0.001279700 0.000924260 0.000355437 6892
+#> 1 gq 0x111b21e00 0.000516567 0.000516567 0.000000000 0
+#> 2 likelihood 0x111b21e00 0.001479350 0.001061390 0.000417962 6892
#> no_chain_stack autodiff_calls no_autodiff_calls
#> 1 0 0 1000
#> 2 6892 6892 1
diff --git a/docs/reference/fit-method-return_codes.html b/docs/reference/fit-method-return_codes.html
index 734fbae7..83a654e0 100644
--- a/docs/reference/fit-method-return_codes.html
+++ b/docs/reference/fit-method-return_codes.html
@@ -130,9 +130,7 @@ Examples
# \dontrun{
# example with return codes all zero
fit_mcmc <- cmdstanr_example("schools", method = "sample")
-#> Warning: 238 of 4000 (6.0%) transitions ended with a divergence.
-#> See https://mc-stan.org/misc/warnings for details.
-#> Warning: 1 of 4 chains had an E-BFMI less than 0.2.
+#> Warning: 72 of 4000 (2.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.
fit_mcmc$return_codes() # should be all zero
#> [1] 0 0 0 0
diff --git a/docs/reference/fit-method-sampler_diagnostics.html b/docs/reference/fit-method-sampler_diagnostics.html
index e83c1bf5..69c92928 100644
--- a/docs/reference/fit-method-sampler_diagnostics.html
+++ b/docs/reference/fit-method-sampler_diagnostics.html
@@ -152,7 +152,7 @@ Examples
fit <- cmdstanr_example("logistic")
sampler_diagnostics <- fit$sampler_diagnostics()
str(sampler_diagnostics)
-#> 'draws_array' num [1:1000, 1:4, 1:6] 2 2 2 2 2 3 2 2 2 3 ...
+#> 'draws_array' num [1:1000, 1:4, 1:6] 2 2 2 2 3 2 2 3 2 2 ...
#> - attr(*, "dimnames")=List of 3
#> ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
#> ..$ chain : chr [1:4] "1" "2" "3" "4"
@@ -162,16 +162,16 @@ Examples
as_draws_df(sampler_diagnostics)
#> # A draws_df: 1000 iterations, 4 chains, and 6 variables
#> treedepth__ divergent__ energy__ accept_stat__ stepsize__ n_leapfrog__
-#> 1 2 0 70 0.76 0.73 3
-#> 2 2 0 69 1.00 0.73 3
-#> 3 2 0 69 1.00 0.73 3
-#> 4 2 0 66 1.00 0.73 3
-#> 5 2 0 66 0.97 0.73 3
-#> 6 3 0 67 0.88 0.73 7
-#> 7 2 0 72 0.74 0.73 3
-#> 8 2 0 73 1.00 0.73 3
-#> 9 2 0 69 1.00 0.73 3
-#> 10 3 0 66 0.95 0.73 7
+#> 1 2 0 66 0.87 0.85 3
+#> 2 2 0 66 0.91 0.85 3
+#> 3 2 0 66 0.99 0.85 3
+#> 4 2 0 65 1.00 0.85 3
+#> 5 3 0 65 0.94 0.85 7
+#> 6 2 0 68 0.83 0.85 7
+#> 7 2 0 66 0.93 0.85 7
+#> 8 3 0 67 0.94 0.85 7
+#> 9 2 0 66 1.00 0.85 3
+#> 10 2 0 68 0.65 0.85 3
#> # ... with 3990 more draws
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
@@ -179,16 +179,16 @@ Examples
fit$sampler_diagnostics(format = "df")
#> # A draws_df: 1000 iterations, 4 chains, and 6 variables
#> treedepth__ divergent__ energy__ accept_stat__ stepsize__ n_leapfrog__
-#> 1 2 0 70 0.76 0.73 3
-#> 2 2 0 69 1.00 0.73 3
-#> 3 2 0 69 1.00 0.73 3
-#> 4 2 0 66 1.00 0.73 3
-#> 5 2 0 66 0.97 0.73 3
-#> 6 3 0 67 0.88 0.73 7
-#> 7 2 0 72 0.74 0.73 3
-#> 8 2 0 73 1.00 0.73 3
-#> 9 2 0 69 1.00 0.73 3
-#> 10 3 0 66 0.95 0.73 7
+#> 1 2 0 66 0.87 0.85 3
+#> 2 2 0 66 0.91 0.85 3
+#> 3 2 0 66 0.99 0.85 3
+#> 4 2 0 65 1.00 0.85 3
+#> 5 3 0 65 0.94 0.85 7
+#> 6 2 0 68 0.83 0.85 7
+#> 7 2 0 66 0.93 0.85 7
+#> 8 3 0 67 0.94 0.85 7
+#> 9 2 0 66 1.00 0.85 3
+#> 10 2 0 68 0.65 0.85 3
#> # ... with 3990 more draws
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
# }
diff --git a/docs/reference/fit-method-save_object.html b/docs/reference/fit-method-save_object.html
index 90155c94..8464814f 100644
--- a/docs/reference/fit-method-save_object.html
+++ b/docs/reference/fit-method-save_object.html
@@ -148,16 +148,16 @@ Examples
#> # A tibble: 105 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -66.0 -65.6 1.43 1.24 -68.6 -64.3 1.00 2220.
-#> 2 alpha 0.383 0.380 0.214 0.213 0.0497 0.741 1.00 4650.
-#> 3 beta[1] -0.669 -0.668 0.251 0.258 -1.08 -0.266 1.00 4431.
-#> 4 beta[2] -0.274 -0.269 0.226 0.224 -0.644 0.0995 1.00 3964.
-#> 5 beta[3] 0.675 0.670 0.269 0.269 0.243 1.13 1.00 3857.
-#> 6 log_lik[1] -0.513 -0.506 0.0969 0.0968 -0.678 -0.368 1.00 4700.
-#> 7 log_lik[2] -0.407 -0.386 0.151 0.144 -0.689 -0.194 1.00 4212.
-#> 8 log_lik[3] -0.496 -0.460 0.215 0.203 -0.888 -0.211 1.00 4110.
-#> 9 log_lik[4] -0.452 -0.436 0.152 0.146 -0.732 -0.233 1.00 4273.
-#> 10 log_lik[5] -1.18 -1.16 0.274 0.268 -1.65 -0.760 1.00 4342.
+#> 1 lp__ -65.9 -65.6 1.40 1.17 -68.7 -64.3 1.00 1893.
+#> 2 alpha 0.380 0.380 0.218 0.216 0.0231 0.740 1.00 4801.
+#> 3 beta[1] -0.668 -0.659 0.248 0.247 -1.09 -0.273 1.00 4573.
+#> 4 beta[2] -0.271 -0.267 0.224 0.229 -0.634 0.0845 1.00 4645.
+#> 5 beta[3] 0.678 0.666 0.266 0.259 0.248 1.12 1.00 4505.
+#> 6 log_lik[1] -0.515 -0.508 0.0990 0.0965 -0.689 -0.362 1.00 4844.
+#> 7 log_lik[2] -0.403 -0.384 0.147 0.139 -0.669 -0.199 1.00 4585.
+#> 8 log_lik[3] -0.495 -0.459 0.218 0.202 -0.898 -0.206 1.00 4890.
+#> 9 log_lik[4] -0.452 -0.433 0.153 0.150 -0.726 -0.239 1.00 4491.
+#> 10 log_lik[5] -1.18 -1.16 0.278 0.275 -1.67 -0.765 1.00 5130.
#> # ℹ 95 more rows
#> # ℹ 1 more variable: ess_tail <dbl>
# }
diff --git a/docs/reference/fit-method-save_output_files.html b/docs/reference/fit-method-save_output_files.html
index f096f70e..8fb2b772 100644
--- a/docs/reference/fit-method-save_output_files.html
+++ b/docs/reference/fit-method-save_output_files.html
@@ -210,33 +210,33 @@ Examples
# \dontrun{
fit <- cmdstanr_example()
fit$output_files()
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081030-1-98eced.csv"
-#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081030-2-98eced.csv"
-#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081030-3-98eced.csv"
-#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081030-4-98eced.csv"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131005-1-212881.csv"
+#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131005-2-212881.csv"
+#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131005-3-212881.csv"
+#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131005-4-212881.csv"
fit$data_file()
-#> [1] "/private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpYtRZP3/temp_libpath119dc23a21f00/cmdstanr/logistic.data.json"
+#> [1] "/private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmps9TW4l/temp_libpath5800f91909/cmdstanr/logistic.data.json"
# just using tempdir for the example
my_dir <- tempdir()
fit$save_output_files(dir = my_dir, basename = "banana")
#> Moved 4 files and set internal paths to new locations:
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/banana-202311081030-1-846feb.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/banana-202311081030-2-846feb.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/banana-202311081030-3-846feb.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/banana-202311081030-4-846feb.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/banana-202312131005-1-932dd0.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/banana-202312131005-2-932dd0.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/banana-202312131005-3-932dd0.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/banana-202312131005-4-932dd0.csv
fit$save_output_files(dir = my_dir, basename = "tomato", timestamp = FALSE)
#> Moved 4 files and set internal paths to new locations:
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/tomato-1-270839.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/tomato-2-270839.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/tomato-3-270839.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/tomato-4-270839.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/tomato-1-1f5444.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/tomato-2-1f5444.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/tomato-3-1f5444.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/tomato-4-1f5444.csv
fit$save_output_files(dir = my_dir, basename = "lettuce", timestamp = FALSE, random = FALSE)
#> Moved 4 files and set internal paths to new locations:
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/lettuce-1.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/lettuce-2.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/lettuce-3.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/lettuce-4.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/lettuce-1.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/lettuce-2.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/lettuce-3.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/lettuce-4.csv
# }
diff --git a/docs/reference/fit-method-summary.html b/docs/reference/fit-method-summary.html
index 6f8199fa..14110b92 100644
--- a/docs/reference/fit-method-summary.html
+++ b/docs/reference/fit-method-summary.html
@@ -165,36 +165,36 @@ Examples
#> # A tibble: 105 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -65.9 -65.6 1.43 1.23 -68.7 -64.3 1.00 2038.
-#> 2 alpha 0.376 0.369 0.215 0.214 0.0214 0.727 1.00 4221.
-#> 3 beta[1] -0.665 -0.659 0.247 0.241 -1.08 -0.263 1.00 4093.
-#> 4 beta[2] -0.275 -0.271 0.224 0.221 -0.652 0.0850 1.00 4052.
-#> 5 beta[3] 0.676 0.669 0.268 0.268 0.244 1.12 1.00 4213.
-#> 6 log_lik[1] -0.517 -0.511 0.0979 0.0959 -0.683 -0.369 1.00 4266.
-#> 7 log_lik[2] -0.404 -0.383 0.147 0.140 -0.669 -0.200 1.00 4613.
-#> 8 log_lik[3] -0.501 -0.466 0.218 0.200 -0.913 -0.209 1.00 4197.
-#> 9 log_lik[4] -0.450 -0.430 0.153 0.145 -0.724 -0.229 1.00 4069.
-#> 10 log_lik[5] -1.18 -1.15 0.278 0.270 -1.67 -0.758 1.00 4386.
+#> 1 lp__ -66.0 -65.7 1.48 1.23 -68.9 -64.3 1.00 1956.
+#> 2 alpha 0.382 0.381 0.221 0.222 0.0263 0.744 1.00 3982.
+#> 3 beta[1] -0.665 -0.661 0.252 0.253 -1.09 -0.264 1.00 4131.
+#> 4 beta[2] -0.274 -0.280 0.226 0.226 -0.640 0.0976 1.00 3816.
+#> 5 beta[3] 0.675 0.673 0.268 0.265 0.238 1.12 1.00 3963.
+#> 6 log_lik[1] -0.514 -0.508 0.0987 0.0980 -0.686 -0.364 1.00 3980.
+#> 7 log_lik[2] -0.408 -0.385 0.153 0.142 -0.690 -0.197 1.00 4263.
+#> 8 log_lik[3] -0.498 -0.467 0.217 0.204 -0.889 -0.206 1.00 3904.
+#> 9 log_lik[4] -0.453 -0.436 0.152 0.148 -0.732 -0.236 1.00 3847.
+#> 10 log_lik[5] -1.18 -1.16 0.285 0.281 -1.68 -0.749 1.00 4407.
#> # ℹ 95 more rows
#> # ℹ 1 more variable: ess_tail <dbl>
fit$print()
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
-#> lp__ -65.92 -65.59 1.43 1.23 -68.71 -64.30 1.00 2038 2638
-#> alpha 0.38 0.37 0.21 0.21 0.02 0.73 1.00 4221 3302
-#> beta[1] -0.67 -0.66 0.25 0.24 -1.08 -0.26 1.00 4092 2987
-#> beta[2] -0.28 -0.27 0.22 0.22 -0.65 0.09 1.00 4052 3349
-#> beta[3] 0.68 0.67 0.27 0.27 0.24 1.12 1.00 4212 2892
-#> log_lik[1] -0.52 -0.51 0.10 0.10 -0.68 -0.37 1.00 4265 3448
-#> log_lik[2] -0.40 -0.38 0.15 0.14 -0.67 -0.20 1.00 4612 3246
-#> log_lik[3] -0.50 -0.47 0.22 0.20 -0.91 -0.21 1.00 4197 3040
-#> log_lik[4] -0.45 -0.43 0.15 0.14 -0.72 -0.23 1.00 4069 2989
-#> log_lik[5] -1.18 -1.15 0.28 0.27 -1.67 -0.76 1.00 4386 2650
+#> lp__ -66.00 -65.68 1.48 1.23 -68.89 -64.30 1.00 1956 2665
+#> alpha 0.38 0.38 0.22 0.22 0.03 0.74 1.00 3981 3124
+#> beta[1] -0.66 -0.66 0.25 0.25 -1.09 -0.26 1.00 4131 3206
+#> beta[2] -0.27 -0.28 0.23 0.23 -0.64 0.10 1.00 3815 2736
+#> beta[3] 0.67 0.67 0.27 0.26 0.24 1.12 1.00 3963 3257
+#> log_lik[1] -0.51 -0.51 0.10 0.10 -0.69 -0.36 1.00 3979 2841
+#> log_lik[2] -0.41 -0.39 0.15 0.14 -0.69 -0.20 1.00 4263 3051
+#> log_lik[3] -0.50 -0.47 0.22 0.20 -0.89 -0.21 1.00 3904 3141
+#> log_lik[4] -0.45 -0.44 0.15 0.15 -0.73 -0.24 1.00 3847 2904
+#> log_lik[5] -1.18 -1.16 0.28 0.28 -1.68 -0.75 1.00 4406 3095
#>
#> # showing 10 of 105 rows (change via 'max_rows' argument or 'cmdstanr_max_rows' option)
fit$print(max_rows = 2) # same as print(fit, max_rows = 2)
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
-#> lp__ -65.92 -65.59 1.43 1.23 -68.71 -64.30 1.00 2038 2638
-#> alpha 0.38 0.37 0.21 0.21 0.02 0.73 1.00 4221 3302
+#> lp__ -66.00 -65.68 1.48 1.23 -68.89 -64.30 1.00 1956 2665
+#> alpha 0.38 0.38 0.22 0.22 0.03 0.74 1.00 3981 3124
#>
#> # showing 2 of 105 rows (change via 'max_rows' argument or 'cmdstanr_max_rows' option)
@@ -203,29 +203,29 @@ Examples
#> # A tibble: 3 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 beta[1] -0.665 -0.659 0.247 0.241 -1.08 -0.263 1.00 4093. 2988.
-#> 2 beta[2] -0.275 -0.271 0.224 0.221 -0.652 0.0850 1.00 4052. 3350.
-#> 3 beta[3] 0.676 0.669 0.268 0.268 0.244 1.12 1.00 4213. 2892.
+#> 1 beta[1] -0.665 -0.661 0.252 0.253 -1.09 -0.264 1.00 4131. 3207.
+#> 2 beta[2] -0.274 -0.280 0.226 0.226 -0.640 0.0976 1.00 3816. 2736.
+#> 3 beta[3] 0.675 0.673 0.268 0.265 0.238 1.12 1.00 3963. 3257.
fit$print(c("alpha", "beta[2]"))
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
-#> alpha 0.38 0.37 0.21 0.21 0.02 0.73 1.00 4221 3302
-#> beta[2] -0.28 -0.27 0.22 0.22 -0.65 0.09 1.00 4052 3349
+#> alpha 0.38 0.38 0.22 0.22 0.03 0.74 1.00 3981 3124
+#> beta[2] -0.27 -0.28 0.23 0.23 -0.64 0.10 1.00 3815 2736
# include all variables but only certain summaries
fit$summary(NULL, c("mean", "sd"))
#> # A tibble: 105 × 3
#> variable mean sd
#> <chr> <dbl> <dbl>
-#> 1 lp__ -65.9 1.43
-#> 2 alpha 0.376 0.215
-#> 3 beta[1] -0.665 0.247
-#> 4 beta[2] -0.275 0.224
-#> 5 beta[3] 0.676 0.268
-#> 6 log_lik[1] -0.517 0.0979
-#> 7 log_lik[2] -0.404 0.147
-#> 8 log_lik[3] -0.501 0.218
-#> 9 log_lik[4] -0.450 0.153
-#> 10 log_lik[5] -1.18 0.278
+#> 1 lp__ -66.0 1.48
+#> 2 alpha 0.382 0.221
+#> 3 beta[1] -0.665 0.252
+#> 4 beta[2] -0.274 0.226
+#> 5 beta[3] 0.675 0.268
+#> 6 log_lik[1] -0.514 0.0987
+#> 7 log_lik[2] -0.408 0.153
+#> 8 log_lik[3] -0.498 0.217
+#> 9 log_lik[4] -0.453 0.152
+#> 10 log_lik[5] -1.18 0.285
#> # ℹ 95 more rows
# can use functions created from formulas
@@ -234,9 +234,9 @@ Examples
#> # A tibble: 3 × 2
#> variable prob_gt_0
#> <chr> <dbl>
-#> 1 beta[1] 0.00225
-#> 2 beta[2] 0.107
-#> 3 beta[3] 0.995
+#> 1 beta[1] 0.00325
+#> 2 beta[2] 0.114
+#> 3 beta[3] 0.997
# can combine user-specified functions with
# the default summary functions
@@ -248,10 +248,10 @@ Examples
#> # A tibble: 4 × 10
#> variable mean median sd mad q2.5 q97.5 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 alpha 0.376 0.369 0.215 0.214 -0.0365 0.800 1.00 4221. 3303.
-#> 2 beta[1] -0.665 -0.659 0.247 0.241 -1.17 -0.200 1.00 4093. 2988.
-#> 3 beta[2] -0.275 -0.271 0.224 0.221 -0.720 0.155 1.00 4052. 3350.
-#> 4 beta[3] 0.676 0.669 0.268 0.268 0.172 1.23 1.00 4213. 2892.
+#> 1 alpha 0.382 0.381 0.221 0.222 -0.0413 0.821 1.00 3982. 3124.
+#> 2 beta[1] -0.665 -0.661 0.252 0.253 -1.18 -0.185 1.00 4131. 3207.
+#> 3 beta[2] -0.274 -0.280 0.226 0.226 -0.715 0.168 1.00 3816. 2736.
+#> 4 beta[3] 0.675 0.673 0.268 0.265 0.170 1.20 1.00 3963. 3257.
# the functions need to calculate the appropriate
# value for a matrix input
diff --git a/docs/reference/fit-method-time.html b/docs/reference/fit-method-time.html
index 474e4a0b..c0a070b8 100644
--- a/docs/reference/fit-method-time.html
+++ b/docs/reference/fit-method-time.html
@@ -140,36 +140,36 @@ Examples
fit_mcmc <- cmdstanr_example("logistic", method = "sample")
fit_mcmc$time()
#> $total
-#> [1] 0.5498919
+#> [1] 0.554847
#>
#> $chains
#> chain_id warmup sampling total
#> 1 1 0.023 0.071 0.094
-#> 2 2 0.023 0.070 0.093
-#> 3 3 0.023 0.074 0.097
-#> 4 4 0.023 0.074 0.097
+#> 2 2 0.022 0.066 0.088
+#> 3 3 0.023 0.071 0.094
+#> 4 4 0.023 0.071 0.094
#>
fit_vb <- cmdstanr_example("logistic", method = "variational")
fit_vb$time()
#> $total
-#> [1] 0.1315289
+#> [1] 0.1302531
#>
fit_mle <- cmdstanr_example("logistic", method = "optimize", jacobian = TRUE)
fit_mle$time()
#> $total
-#> [1] 0.1302979
+#> [1] 0.1320548
#>
# use fit_mle to draw samples from laplace approximation
fit_laplace <- cmdstanr_example("logistic", method = "laplace", mode = fit_mle)
fit_laplace$time() # just time for drawing sample not for running optimize
#> $total
-#> [1] 0.136251
+#> [1] 0.126792
#>
fit_laplace$time()$total + fit_mle$time()$total # total time
-#> [1] 0.2665489
+#> [1] 0.2588468
# }
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 1036551c..c1a096fe 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -160,7 +160,7 @@ Running CmdStan from R laplace()
- Run Stan's laplace algorithm
Run Stan's Laplace algorithm
diff --git a/docs/reference/model-method-check_syntax.html b/docs/reference/model-method-check_syntax.html
index 14ad8c59..6ec6f59e 100644
--- a/docs/reference/model-method-check_syntax.html
+++ b/docs/reference/model-method-check_syntax.html
@@ -125,7 +125,7 @@ Arguments
- pedantic
(logical) Should pedantic mode be turned on? The default is
FALSE. Pedantic mode attempts to warn you about potential issues in your
-Stan program beyond syntax errors. For details see the Pedantic mode chapter in
+Stan program beyond syntax errors. For details see the Pedantic mode chapter in
the Stan Reference Manual.
Examples
# pedantic mode will warn that lambda should be constrained to be positive
# and that lambda has no prior distribution
mod$check_syntax(pedantic = TRUE)
-#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
+#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
#> poisson distribution is given parameter lambda as a rate parameter
#> (argument 1), but lambda was not constrained to be strictly positive.
#> Warning: The parameter lambda has no priors. This means either no prior is
diff --git a/docs/reference/model-method-compile.html b/docs/reference/model-method-compile.html
index 2ae3d9c3..e5a50034 100644
--- a/docs/reference/model-method-compile.html
+++ b/docs/reference/model-method-compile.html
@@ -171,7 +171,7 @@ Arguments
- pedantic
(logical) Should pedantic mode be turned on? The default is
FALSE. Pedantic mode attempts to warn you about potential issues in your
-Stan program beyond syntax errors. For details see the Pedantic mode chapter in
+Stan program beyond syntax errors. For details see the Pedantic mode chapter in
the Stan Reference Manual. Note: to do a pedantic check for a model
without compiling it or for a model that is already compiled the
$check_syntax() method can be used instead.
Examples
}
")
mod <- cmdstan_model(file_pedantic, pedantic = TRUE)
-#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model-11d996d1c906f.stan', line 6, column 2: Parameter
+#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/model-5f0d242e1d46.stan', line 6, column 2: Parameter
#> sigma is given a exponential distribution, which has strictly positive
#> support, but sigma was not constrained to be strictly positive.
diff --git a/docs/reference/model-method-diagnose.html b/docs/reference/model-method-diagnose.html
index e4120b50..9a09d308 100644
--- a/docs/reference/model-method-diagnose.html
+++ b/docs/reference/model-method-diagnose.html
@@ -165,10 +165,10 @@ Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.
A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -245,11 +245,11 @@
Examples
# retrieve the gradients
test$gradients()
-#> param_idx value model finite_diff error
-#> 1 0 0.466346 3.5478 3.5478 1.64210e-08
-#> 2 1 1.496850 -35.5646 -35.5646 6.49428e-09
-#> 3 2 1.385000 -27.1027 -27.1027 -1.36431e-08
-#> 4 3 -0.467569 14.0233 14.0233 1.05492e-08
+#> param_idx value model finite_diff error
+#> 1 0 -1.0700800 30.35590 30.35590 -8.78525e-09
+#> 2 1 0.5670250 -22.14500 -22.14500 9.99824e-09
+#> 3 2 0.0466329 -10.07860 -10.07860 1.68498e-08
+#> 4 3 1.1724800 -5.09201 -5.09201 2.79421e-10
# }
diff --git a/docs/reference/model-method-format.html b/docs/reference/model-method-format.html
index b0a71308..3e6ef100 100644
--- a/docs/reference/model-method-format.html
+++ b/docs/reference/model-method-format.html
@@ -212,7 +212,7 @@ Examples
# overwrite the original file instead of just printing it
mod$format(canonicalize = list("deprecations"), overwrite_file = TRUE)
-#> Old version of the model stored to /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model_1fc88c86300d78667dd3e476a636c279.stan.bak-20231108103429.
+#> Old version of the model stored to /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/model_1fc88c86300d78667dd3e476a636c279.stan.bak-20231213100754.
mod$compile()
diff --git a/docs/reference/model-method-laplace-1.png b/docs/reference/model-method-laplace-1.png
index 01b1f50d..aaf21c13 100644
Binary files a/docs/reference/model-method-laplace-1.png and b/docs/reference/model-method-laplace-1.png differ
diff --git a/docs/reference/model-method-laplace.html b/docs/reference/model-method-laplace.html
index 6a0d5658..20880c2f 100644
--- a/docs/reference/model-method-laplace.html
+++ b/docs/reference/model-method-laplace.html
@@ -1,5 +1,5 @@
-Run Stan's laplace algorithm — model-method-laplace • cmdstanr Run Stan's Laplace algorithm — model-method-laplace • cmdstanr
- Run Stan's laplace algorithm
+ Run Stan's Laplace algorithm
Source: R/model.R
model-method-laplace.Rd
@@ -192,10 +192,10 @@ Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.
A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -332,9 +332,9 @@
Examples
stan_data <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))
fit_mode <- mod$optimize(data = stan_data, jacobian = TRUE)
-#> Initial log joint probability = -6.78622
+#> Initial log joint probability = -8.0994
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 4 -6.74802 0.000186334 5.91326e-07 1 1 7
+#> 5 -6.74802 0.000245062 1.61833e-06 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -357,15 +357,15 @@ Examples
#> # A tibble: 3 × 7
#> variable mean median sd mad q5 q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.24 -6.98 0.677 0.309 -8.74 -6.75
-#> 2 lp_approx__ -0.505 -0.230 0.686 0.308 -1.92 -0.00197
-#> 3 theta 0.269 0.254 0.123 0.125 0.0982 0.500
+#> 1 lp__ -7.25 -6.97 0.724 0.305 -8.72 -6.75
+#> 2 lp_approx__ -0.516 -0.225 0.747 0.306 -1.93 -0.00324
+#> 3 theta 0.267 0.250 0.124 0.120 0.0954 0.499
# if mode isn't specified optimize is run internally first
fit_laplace <- mod$laplace(data = stan_data)
-#> Initial log joint probability = -6.7528
+#> Initial log joint probability = -15.1067
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 3 -6.74802 0.000769281 2.15262e-05 1 1 6
+#> 5 -6.74802 0.00203152 6.06231e-05 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -387,9 +387,9 @@ Examples
#> # A tibble: 3 × 7
#> variable mean median sd mad q5 q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.26 -6.95 0.755 0.289 -8.76 -6.75
-#> 2 lp_approx__ -0.518 -0.203 0.755 0.285 -2.15 -0.00170
-#> 3 theta 0.267 0.250 0.125 0.115 0.0947 0.505
+#> 1 lp__ -7.28 -6.98 0.782 0.316 -8.84 -6.75
+#> 2 lp_approx__ -0.531 -0.231 0.756 0.320 -1.96 -0.00280
+#> 3 theta 0.273 0.251 0.128 0.123 0.0984 0.510
# plot approximate posterior
bayesplot::mcmc_hist(fit_laplace$draws("theta"))
diff --git a/docs/reference/model-method-optimize.html b/docs/reference/model-method-optimize.html
index f88b0551..78b2a042 100644
--- a/docs/reference/model-method-optimize.html
+++ b/docs/reference/model-method-optimize.html
@@ -194,10 +194,10 @@ Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -531,12 +531,12 @@
Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -11.84
+#> Initial log joint probability = -7.17234
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 5 -6.74802 0.00117606 2.01092e-05 1 1 8
+#> 5 -6.74802 0.000196979 1.66919e-07 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -569,9 +569,9 @@ Examples
#> # A tibble: 3 × 7
#> variable mean median sd mad q5 q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.23 -6.96 0.703 0.291 -8.51 -6.75
-#> 2 lp_approx__ -0.490 -0.218 0.693 0.294 -1.85 -0.00308
-#> 3 theta 0.268 0.247 0.122 0.119 0.0992 0.495
+#> 1 lp__ -7.26 -6.98 0.759 0.313 -8.67 -6.75
+#> 2 lp_approx__ -0.521 -0.232 0.759 0.318 -2.07 -0.00238
+#> 3 theta 0.266 0.248 0.125 0.123 0.0975 0.496
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -580,8 +580,8 @@ Examples
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
-#> Gradient evaluation took 8e-06 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
+#> Gradient evaluation took 6e-06 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
@@ -640,6 +640,53 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -16.937625
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.488e-03 4.162e-05 1.000e+00 1.000e+00 126 -6.227e+00 -6.227e+00
+#> Path [1] :Best Iter: [2] ELBO (-6.211714) evaluations: (126)
+#> Path [2] :Initial log joint density = -6.798413
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 3.762e-03 4.627e-05 9.519e-01 9.519e-01 76 -6.195e+00 -6.195e+00
+#> Path [2] :Best Iter: [3] ELBO (-6.194747) evaluations: (76)
+#> Path [3] :Initial log joint density = -7.054440
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 2.741e-03 5.225e-05 1.000e+00 1.000e+00 101 -6.187e+00 -6.187e+00
+#> Path [3] :Best Iter: [4] ELBO (-6.186757) evaluations: (101)
+#> Path [4] :Initial log joint density = -7.467690
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.049e-04 4.080e-07 1.000e+00 1.000e+00 126 -6.224e+00 -6.224e+00
+#> Path [4] :Best Iter: [2] ELBO (-6.213804) evaluations: (126)
+#> Path [5] :Initial log joint density = -14.817294
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.041e-03 5.999e-05 1.000e+00 1.000e+00 126 -6.271e+00 -6.271e+00
+#> Path [5] :Best Iter: [3] ELBO (-6.188344) evaluations: (126)
+#> Path [6] :Initial log joint density = -7.375208
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 5.530e-04 1.038e-06 1.000e+00 1.000e+00 126 -6.229e+00 -6.229e+00
+#> Path [6] :Best Iter: [2] ELBO (-6.203592) evaluations: (126)
+#> Path [7] :Initial log joint density = -6.748023
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 2 -6.748e+00 9.521e-04 6.579e-07 1.000e+00 1.000e+00 51 -6.231e+00 -6.231e+00
+#> Path [7] :Best Iter: [2] ELBO (-6.231405) evaluations: (51)
+#> Path [8] :Initial log joint density = -11.312958
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.017e-03 1.576e-05 1.000e+00 1.000e+00 126 -6.304e+00 -6.304e+00
+#> Path [8] :Best Iter: [3] ELBO (-6.185214) evaluations: (126)
+#> Path [9] :Initial log joint density = -6.881067
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 9.878e-04 9.538e-06 1.000e+00 1.000e+00 101 -6.173e+00 -6.173e+00
+#> Path [9] :Best Iter: [4] ELBO (-6.173184) evaluations: (101)
+#> Path [10] :Initial log joint density = -7.173191
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.980e-04 1.685e-07 1.000e+00 1.000e+00 126 -6.176e+00 -6.176e+00
+#> Path [10] :Best Iter: [5] ELBO (-6.175625) evaluations: (126)
+#> Total log probability function evaluations:1235
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
diff --git a/docs/reference/model-method-pathfinder.html b/docs/reference/model-method-pathfinder.html
index f6423f16..e5caa27c 100644
--- a/docs/reference/model-method-pathfinder.html
+++ b/docs/reference/model-method-pathfinder.html
@@ -2,12 +2,15 @@
Run Stan's Pathfinder Variational Inference Algorithm — model-method-pathfinder • cmdstanr CmdStanModel object runs
Stan's Pathfinder algorithms. Pathfinder is a variational method for
approximately sampling from differentiable log densities. Starting from a
-random initialization, Pathfinder locates normal approximations to the
-target density along a quasi-Newton optimization path, with local
-covariance estimated using the negative inverse Hessian estimates produced
-by the L-BFGS optimizer. Pathfinder returns draws from the Gaussian
-approximation with the lowest estimated Kullback-Leibler (KL) divergence to
-the true posterior. See the
+random initialization, Pathfinder locates normal approximations
+to the target density along a quasi-Newton optimization path in
+the unconstrained space, with local covariance estimated using
+the negative inverse Hessian estimates produced by the LBFGS
+optimizer. Pathfinder selects the normal approximation with the
+lowest estimated Kullback-Leibler (KL) divergence to the true
+posterior. Finally Pathfinder draws from that normal
+approximation and returns the draws transformed to the
+constrained scale. See the
CmdStan User’s Guide
for more details.
Any argument left as NULL will default to the default value used by the
@@ -203,10 +209,10 @@
Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -310,8 +316,9 @@
Arguments
draws
(positive integer) Number of draws to return after performing
-pareto smooted importance sampling (PSIS). This must be smaller than
-single_path_draws * num_paths.
single_path_draws * num_paths (future versions of CmdStan will throw a
+warning).
num_paths
@@ -548,12 +555,12 @@ Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -8.53772
+#> Initial log joint probability = -11.5709
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 5 -6.74802 0.000313669 2.44314e-06 1 1 8
+#> 5 -6.74802 0.00107647 1.72219e-05 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -584,11 +591,11 @@ Examples
#> Finished in 0.1 seconds.
fit_laplace$summary()
#> # A tibble: 3 × 7
-#> variable mean median sd mad q5 q95
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.24 -6.97 0.690 0.299 -8.66 -6.75
-#> 2 lp_approx__ -0.502 -0.223 0.693 0.303 -1.94 -0.00213
-#> 3 theta 0.267 0.247 0.123 0.119 0.0995 0.501
+#> variable mean median sd mad q5 q95
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -7.23 -6.99 0.637 0.323 -8.54 -6.75
+#> 2 lp_approx__ -0.486 -0.238 0.644 0.323 -1.80 -0.00166
+#> 3 theta 0.268 0.252 0.121 0.126 0.104 0.492
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -597,8 +604,8 @@ Examples
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
-#> Gradient evaluation took 7e-06 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
+#> Gradient evaluation took 6e-06 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
@@ -657,6 +664,53 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -14.470660
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.026e-03 5.797e-05 1.000e+00 1.000e+00 126 -6.259e+00 -6.259e+00
+#> Path [1] :Best Iter: [4] ELBO (-6.211388) evaluations: (126)
+#> Path [2] :Initial log joint density = -9.064681
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 3.457e-04 2.927e-06 1.000e+00 1.000e+00 126 -6.253e+00 -6.253e+00
+#> Path [2] :Best Iter: [3] ELBO (-6.227371) evaluations: (126)
+#> Path [3] :Initial log joint density = -15.152638
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.028e-03 6.063e-05 1.000e+00 1.000e+00 126 -6.230e+00 -6.230e+00
+#> Path [3] :Best Iter: [4] ELBO (-6.211247) evaluations: (126)
+#> Path [4] :Initial log joint density = -10.220491
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 7.043e-04 8.900e-06 1.000e+00 1.000e+00 126 -6.194e+00 -6.194e+00
+#> Path [4] :Best Iter: [5] ELBO (-6.193620) evaluations: (126)
+#> Path [5] :Initial log joint density = -6.766009
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 2.724e-03 1.458e-04 1.000e+00 1.000e+00 76 -6.232e+00 -6.232e+00
+#> Path [5] :Best Iter: [3] ELBO (-6.232284) evaluations: (76)
+#> Path [6] :Initial log joint density = -7.304442
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 4.014e-04 5.879e-07 1.000e+00 1.000e+00 126 -6.247e+00 -6.247e+00
+#> Path [6] :Best Iter: [4] ELBO (-6.212233) evaluations: (126)
+#> Path [7] :Initial log joint density = -7.233791
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.804e-04 3.115e-07 1.000e+00 1.000e+00 126 -6.275e+00 -6.275e+00
+#> Path [7] :Best Iter: [4] ELBO (-6.122234) evaluations: (126)
+#> Path [8] :Initial log joint density = -11.942008
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.223e-03 2.161e-05 1.000e+00 1.000e+00 126 -6.232e+00 -6.232e+00
+#> Path [8] :Best Iter: [3] ELBO (-6.217220) evaluations: (126)
+#> Path [9] :Initial log joint density = -7.438064
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 9.817e-05 3.665e-07 1.000e+00 1.000e+00 126 -6.268e+00 -6.268e+00
+#> Path [9] :Best Iter: [3] ELBO (-6.213533) evaluations: (126)
+#> Path [10] :Initial log joint density = -8.992806
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 3.448e-04 2.904e-06 1.000e+00 1.000e+00 126 -6.247e+00 -6.247e+00
+#> Path [10] :Best Iter: [2] ELBO (-6.141839) evaluations: (126)
+#> Total log probability function evaluations:1360
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
diff --git a/docs/reference/model-method-sample.html b/docs/reference/model-method-sample.html
index 02207960..7925e4b4 100644
--- a/docs/reference/model-method-sample.html
+++ b/docs/reference/model-method-sample.html
@@ -210,10 +210,10 @@ Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -659,12 +659,12 @@
Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -7.01351
+#> Initial log joint probability = -11.0695
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 4 -6.74802 0.000544739 0.000133582 0.9055 0.9055 7
+#> 5 -6.74802 0.000954615 1.42918e-05 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -695,11 +695,11 @@ Examples
#> Finished in 0.1 seconds.
fit_laplace$summary()
#> # A tibble: 3 × 7
-#> variable mean median sd mad q5 q95
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.24 -6.98 0.692 0.314 -8.61 -6.75
-#> 2 lp_approx__ -0.500 -0.233 0.688 0.317 -1.89 -0.00172
-#> 3 theta 0.269 0.248 0.123 0.122 0.0989 0.503
+#> variable mean median sd mad q5 q95
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 lp__ -7.23 -6.97 0.710 0.297 -8.57 -6.75
+#> 2 lp_approx__ -0.490 -0.220 0.696 0.300 -1.90 -0.00171
+#> 3 theta 0.270 0.252 0.123 0.120 0.102 0.495
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -708,8 +708,8 @@ Examples
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
-#> Gradient evaluation took 7e-06 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
+#> Gradient evaluation took 6e-06 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
@@ -768,6 +768,53 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -10.398597
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 7.601e-04 1.002e-05 1.000e+00 1.000e+00 126 -6.246e+00 -6.246e+00
+#> Path [1] :Best Iter: [4] ELBO (-6.213236) evaluations: (126)
+#> Path [2] :Initial log joint density = -7.343771
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 5.658e-03 1.740e-04 1.000e+00 1.000e+00 101 -6.270e+00 -6.270e+00
+#> Path [2] :Best Iter: [3] ELBO (-6.236555) evaluations: (101)
+#> Path [3] :Initial log joint density = -7.253954
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 4.780e-03 1.316e-04 1.000e+00 1.000e+00 101 -6.293e+00 -6.293e+00
+#> Path [3] :Best Iter: [3] ELBO (-6.210231) evaluations: (101)
+#> Path [4] :Initial log joint density = -6.884985
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 1.025e-03 1.015e-05 1.000e+00 1.000e+00 101 -6.254e+00 -6.254e+00
+#> Path [4] :Best Iter: [4] ELBO (-6.254382) evaluations: (101)
+#> Path [5] :Initial log joint density = -10.549385
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 8.063e-04 1.099e-05 1.000e+00 1.000e+00 126 -6.155e+00 -6.155e+00
+#> Path [5] :Best Iter: [5] ELBO (-6.154812) evaluations: (126)
+#> Path [6] :Initial log joint density = -11.021697
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 9.418e-04 1.400e-05 1.000e+00 1.000e+00 126 -6.261e+00 -6.261e+00
+#> Path [6] :Best Iter: [4] ELBO (-6.204645) evaluations: (126)
+#> Path [7] :Initial log joint density = -10.721189
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 8.573e-04 1.209e-05 1.000e+00 1.000e+00 126 -6.195e+00 -6.195e+00
+#> Path [7] :Best Iter: [5] ELBO (-6.194637) evaluations: (126)
+#> Path [8] :Initial log joint density = -11.729882
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.125e-03 1.854e-05 1.000e+00 1.000e+00 126 -6.195e+00 -6.195e+00
+#> Path [8] :Best Iter: [4] ELBO (-6.193964) evaluations: (126)
+#> Path [9] :Initial log joint density = -8.375820
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.924e-04 2.169e-06 1.000e+00 1.000e+00 126 -6.198e+00 -6.198e+00
+#> Path [9] :Best Iter: [2] ELBO (-6.188312) evaluations: (126)
+#> Path [10] :Initial log joint density = -7.041152
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 4 -6.748e+00 2.604e-03 4.795e-05 1.000e+00 1.000e+00 101 -6.233e+00 -6.233e+00
+#> Path [10] :Best Iter: [2] ELBO (-6.214834) evaluations: (101)
+#> Total log probability function evaluations:1310
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
diff --git a/docs/reference/model-method-sample_mpi.html b/docs/reference/model-method-sample_mpi.html
index 5dd06c42..6247b07a 100644
--- a/docs/reference/model-method-sample_mpi.html
+++ b/docs/reference/model-method-sample_mpi.html
@@ -1,7 +1,7 @@
Run Stan's MCMC algorithms with MPI — model-method-sample_mpi • cmdstanr
The $sample_mpi() method of a CmdStanModel object is
identical to the $sample() method but with support for
-MPI. The target audience for MPI are
+MPI (message passing interface). The target audience for MPI are
those with large computer clusters. For other users, the
$sample() method provides both parallelization of
chains and threading support for within-chain parallelization.
@@ -247,10 +247,10 @@ Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.
A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -467,8 +467,8 @@
See also
documentation and tutorials.
The Stan and CmdStan documentation:
Stan documentation: mc-stan.org/users/documentation
CmdStan User’s Guide: mc-stan.org/docs/cmdstan-guide
The Stan Math Library's MPI documentation
-(mc-stan.org/math/mpi) for more
+
The Stan Math Library's documentation
+(mc-stan.org/math) for more
details on MPI support in Stan.
Other CmdStanModel methods:
model-method-check_syntax,
diff --git a/docs/reference/model-method-variational.html b/docs/reference/model-method-variational.html
index 7aee6bce..ed9fcbc4 100644
--- a/docs/reference/model-method-variational.html
+++ b/docs/reference/model-method-variational.html
@@ -197,10 +197,10 @@
Arguments
write_stan_json() to write R objects to JSON files compatible with
CmdStan.A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
@@ -534,12 +534,12 @@
Examples
#> 1 lp__ -5.00
#> 2 theta 0.2
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -7.23249
+#> Initial log joint probability = -11.7793
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
-#> 4 -6.74802 0.00456563 0.000121975 1 1 7
+#> 5 -6.74802 0.00114772 1.92382e-05 1 1 8
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
@@ -572,9 +572,9 @@ Examples
#> # A tibble: 3 × 7
#> variable mean median sd mad q5 q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 lp__ -7.25 -6.99 0.712 0.326 -8.66 -6.75
-#> 2 lp_approx__ -0.506 -0.244 0.715 0.329 -1.94 -0.00187
-#> 3 theta 0.269 0.250 0.124 0.126 0.101 0.500
+#> 1 lp__ -7.22 -6.96 0.669 0.294 -8.51 -6.75
+#> 2 lp_approx__ -0.470 -0.213 0.645 0.295 -1.81 -0.00204
+#> 3 theta 0.267 0.247 0.121 0.118 0.104 0.493
# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -583,8 +583,8 @@ Examples
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
-#> Gradient evaluation took 7e-06 seconds
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
+#> Gradient evaluation took 5e-06 seconds
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
@@ -643,6 +643,54 @@ Examples
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+ history_size=50, max_lbfgs_iters=100)
+#> Path [1] :Initial log joint density = -7.711026
+#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.614e-04 8.188e-07 1.000e+00 1.000e+00 126 -6.206e+00 -6.206e+00
+#> Path [1] :Best Iter: [5] ELBO (-6.205511) evaluations: (126)
+#> Path [2] :Initial log joint density = -12.450513
+#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.452e-03 2.960e-05 1.000e+00 1.000e+00 126 -6.251e+00 -6.251e+00
+#> Path [2] :Best Iter: [4] ELBO (-6.229238) evaluations: (126)
+#> Path [3] :Initial log joint density = -7.647405
+#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.466e-04 7.009e-07 1.000e+00 1.000e+00 126 -6.193e+00 -6.193e+00
+#> Path [3] :Best Iter: [3] ELBO (-6.164986) evaluations: (126)
+#> Path [4] :Initial log joint density = -15.036045
+#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.036e-03 6.056e-05 1.000e+00 1.000e+00 126 -6.235e+00 -6.235e+00
+#> Path [4] :Best Iter: [2] ELBO (-6.225650) evaluations: (126)
+#> Path [5] :Initial log joint density = -6.760365
+#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 1.911e-03 8.518e-05 1.000e+00 1.000e+00 76 -6.247e+00 -6.247e+00
+#> Path [5] :Best Iter: [3] ELBO (-6.246721) evaluations: (76)
+#> Path [6] :Initial log joint density = -6.826898
+#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 3 -6.748e+00 5.423e-03 1.282e-04 9.360e-01 9.360e-01 76 -6.169e+00 -6.169e+00
+#> Path [6] :Best Iter: [3] ELBO (-6.168890) evaluations: (76)
+#> Path [7] :Initial log joint density = -8.134235
+#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 2.517e-04 1.691e-06 1.000e+00 1.000e+00 126 -6.243e+00 -6.243e+00
+#> Path [7] :Best Iter: [3] ELBO (-6.175493) evaluations: (126)
+#> Path [8] :Initial log joint density = -7.511494
+#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 9.446e-04 2.687e-06 1.000e+00 1.000e+00 126 -6.214e+00 -6.214e+00
+#> Path [8] :Best Iter: [2] ELBO (-6.197279) evaluations: (126)
+#> Path [9] :Initial log joint density = -11.516990
+#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.065e-03 1.693e-05 1.000e+00 1.000e+00 126 -6.244e+00 -6.244e+00
+#> Path [9] :Best Iter: [3] ELBO (-6.194623) evaluations: (126)
+#> Path [10] :Initial log joint density = -17.778190
+#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+#> 5 -6.748e+00 1.013e-03 2.414e-05 1.000e+00 1.000e+00 126 -6.262e+00 -6.262e+00
+#> Path [10] :Best Iter: [4] ELBO (-6.170410) evaluations: (126)
+#> Total log probability function evaluations:1310
+#> Pareto k value (0.820024) is greater than 0.7. Importance resampling was not able to improve the approximation, which may indicate that the approximation itself is poor.
+#> Finished in 0.1 seconds.
+
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
diff --git a/docs/reference/read_cmdstan_csv.html b/docs/reference/read_cmdstan_csv.html
index d5b0c50d..d6b8fb74 100644
--- a/docs/reference/read_cmdstan_csv.html
+++ b/docs/reference/read_cmdstan_csv.html
@@ -223,10 +223,10 @@ Examples
fit1 <- cmdstanr_example("logistic", method = "sample", save_warmup = TRUE)
csv_files <- fit1$output_files()
print(csv_files)
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-1-32a06f.csv"
-#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-2-32a06f.csv"
-#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-3-32a06f.csv"
-#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-4-32a06f.csv"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131008-1-642e8e.csv"
+#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131008-2-642e8e.csv"
+#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131008-3-642e8e.csv"
+#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131008-4-642e8e.csv"
# Creating fitting model objects
@@ -234,9 +234,9 @@ Examples
fit2 <- as_cmdstan_fit(csv_files)
fit2$print("beta")
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
-#> beta[1] -0.67 -0.66 0.25 0.24 -1.08 -0.28 1.00 4402 2852
-#> beta[2] -0.27 -0.27 0.22 0.22 -0.65 0.09 1.00 4396 2793
-#> beta[3] 0.68 0.67 0.27 0.26 0.27 1.12 1.00 4545 3039
+#> beta[1] -0.67 -0.66 0.25 0.25 -1.09 -0.26 1.00 4234 3123
+#> beta[2] -0.28 -0.27 0.23 0.23 -0.65 0.09 1.00 4067 3185
+#> beta[3] 0.69 0.67 0.27 0.27 0.26 1.14 1.00 4291 3208
# Using read_cmdstan_csv
#
@@ -248,7 +248,7 @@ Examples
#> ..$ stan_version_major : num 2
#> ..$ stan_version_minor : num 33
#> ..$ stan_version_patch : num 0
-#> ..$ start_datetime : chr "2023-11-08 17:35:02 UTC"
+#> ..$ start_datetime : chr "2023-12-13 17:08:27 UTC"
#> ..$ method : chr "sample"
#> ..$ save_warmup : num 1
#> ..$ thin : num 1
@@ -265,14 +265,14 @@ Examples
#> ..$ num_chains : num 1
#> ..$ id : num [1:4] 1 2 3 4
#> ..$ init : num [1:4] 2 2 2 2
-#> ..$ seed : num 1.63e+09
+#> ..$ seed : num 1.1e+09
#> ..$ refresh : num 100
#> ..$ sig_figs : num -1
-#> ..$ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081035-1-02259b.csv"
+#> ..$ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131008-1-0e2cb1.csv"
#> ..$ stanc_version : chr "stanc3 v2.33.1"
#> ..$ sampler_diagnostics : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ...
#> ..$ variables : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
-#> ..$ step_size_adaptation: num [1:4] 0.731 0.842 0.658 0.727
+#> ..$ step_size_adaptation: num [1:4] 0.761 0.758 0.859 0.765
#> ..$ model_name : chr "logistic_model"
#> ..$ adapt_engaged : num 1
#> ..$ adapt_delta : num 0.8
@@ -283,9 +283,9 @@ Examples
#> ..$ threads_per_chain : num 1
#> ..$ time :'data.frame': 4 obs. of 4 variables:
#> .. ..$ chain_id: num [1:4] 1 2 3 4
-#> .. ..$ warmup : num [1:4] 0.071 0.073 0.078 0.075
-#> .. ..$ sampling: num [1:4] 0.075 0.089 0.078 0.073
-#> .. ..$ total : num [1:4] 0.146 0.162 0.156 0.148
+#> .. ..$ warmup : num [1:4] 0.071 0.073 0.072 0.069
+#> .. ..$ sampling: num [1:4] 0.068 0.068 0.065 0.068
+#> .. ..$ total : num [1:4] 0.139 0.141 0.137 0.137
#> ..$ stan_variable_sizes :List of 4
#> .. ..$ lp__ : num 1
#> .. ..$ alpha : num 1
@@ -297,35 +297,35 @@ Examples
#> ..$ total : int NA
#> ..$ chains:'data.frame': 4 obs. of 4 variables:
#> .. ..$ chain_id: num [1:4] 1 2 3 4
-#> .. ..$ warmup : num [1:4] 0.071 0.073 0.078 0.075
-#> .. ..$ sampling: num [1:4] 0.075 0.089 0.078 0.073
-#> .. ..$ total : num [1:4] 0.146 0.162 0.156 0.148
+#> .. ..$ warmup : num [1:4] 0.071 0.073 0.072 0.069
+#> .. ..$ sampling: num [1:4] 0.068 0.068 0.065 0.068
+#> .. ..$ total : num [1:4] 0.139 0.141 0.137 0.137
#> $ inv_metric :List of 4
-#> ..$ 1: num [1:4] 0.0464 0.0532 0.0478 0.0741
-#> ..$ 2: num [1:4] 0.0374 0.0624 0.0482 0.0688
-#> ..$ 3: num [1:4] 0.042 0.0545 0.0473 0.0754
-#> ..$ 4: num [1:4] 0.0411 0.0563 0.0539 0.0609
+#> ..$ 1: num [1:4] 0.0469 0.0641 0.0523 0.0688
+#> ..$ 2: num [1:4] 0.0438 0.0555 0.0497 0.0685
+#> ..$ 3: num [1:4] 0.0457 0.0612 0.0451 0.0706
+#> ..$ 4: num [1:4] 0.0436 0.0627 0.0506 0.0832
#> $ step_size :List of 4
-#> ..$ 1: num 0.731
-#> ..$ 2: num 0.842
-#> ..$ 3: num 0.658
-#> ..$ 4: num 0.727
-#> $ warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -78.1 -78.1 -78.1 -64.4 -66.7 ...
+#> ..$ 1: num 0.761
+#> ..$ 2: num 0.758
+#> ..$ 3: num 0.859
+#> ..$ 4: num 0.765
+#> $ warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -72 -72 -72 -71.3 -68.2 ...
#> ..- attr(*, "dimnames")=List of 3
#> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
#> .. ..$ chain : chr [1:4] "1" "2" "3" "4"
#> .. ..$ variable : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
-#> $ post_warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -68.5 -66.2 -68 -68.3 -67.2 ...
+#> $ post_warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -64.3 -64.3 -65.7 -67.2 -66 ...
#> ..- attr(*, "dimnames")=List of 3
#> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
#> .. ..$ chain : chr [1:4] "1" "2" "3" "4"
#> .. ..$ variable : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
-#> $ warmup_sampler_diagnostics : 'draws_array' num [1:1000, 1:4, 1:6] 1 0 0 1 0.515 ...
+#> $ warmup_sampler_diagnostics : 'draws_array' num [1:1000, 1:4, 1:6] 1 0 0 0.597 1 ...
#> ..- attr(*, "dimnames")=List of 3
#> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
#> .. ..$ chain : chr [1:4] "1" "2" "3" "4"
#> .. ..$ variable : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ...
-#> $ post_warmup_sampler_diagnostics: 'draws_array' num [1:1000, 1:4, 1:6] 0.856 1 0.856 0.984 1 ...
+#> $ post_warmup_sampler_diagnostics: 'draws_array' num [1:1000, 1:4, 1:6] 0.971 0.97 0.742 0.882 0.87 ...
#> ..- attr(*, "dimnames")=List of 3
#> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
#> .. ..$ chain : chr [1:4] "1" "2" "3" "4"
diff --git a/docs/reference/write_stan_file.html b/docs/reference/write_stan_file.html
index 10ffeb6e..e1459132 100644
--- a/docs/reference/write_stan_file.html
+++ b/docs/reference/write_stan_file.html
@@ -188,7 +188,7 @@ Examples
f <- write_stan_file(stan_program)
print(f)
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model_7f12fc190dd23b0e462f7d73040dd97e.stan"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/model_7f12fc190dd23b0e462f7d73040dd97e.stan"
lines <- readLines(f)
print(lines)
diff --git a/docs/reference/write_stan_json.html b/docs/reference/write_stan_json.html
index d09ca22c..b7b128ef 100644
--- a/docs/reference/write_stan_json.html
+++ b/docs/reference/write_stan_json.html
@@ -167,13 +167,13 @@ Examples
#> "N": 5,
#> "K": 2,
#> "x": [
-#> [0.539316345307751, 1.20165111819522],
-#> [0.027826781785157, -0.219847554976374],
-#> [-0.171509078741447, 1.08053600860179],
-#> [1.01158266854045, 0.0611935712707024],
-#> [-0.426238537104135, 0.270940591850318]
+#> [0.281077678546936, -1.18908686575029],
+#> [0.858726751146771, 0.46461805175525],
+#> [-0.700205759803986, -0.115709696625946],
+#> [1.45853659080418, 0.995745564145492],
+#> [-0.671485199795285, -0.298752493529616]
#> ],
-#> "y": [10, 8, 13, 10, 12],
+#> "y": [12, 6, 11, 9, 11],
#> "z": [1, 0]
#> }
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index 6866e056..74c96150 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -3,6 +3,9 @@
https://mc-stan.org/cmdstanr/404.html
+
+ https://mc-stan.org/cmdstanr/CONTRIBUTING.html
+
https://mc-stan.org/cmdstanr/LICENSE-text.html