Use natural parameters in all examples

R/epinow.R

@@ -56,7 +56,11 @@
 #' )
 #' # set an example reporting delay. In practice this should use an estimate
 #' # from the literature or be estimated from data
-#' reporting_delay <- LogNormal(mean = 2, sd = 1, max = 10)
+#' reporting_delay <- LogNormal(
+#'    meanlog = Fixed(2),
+#'    sdlog = Fixed(1),
+#'    max = 10
+#' )
 #'
 #' # example case data
 #' reported_cases <- example_confirmed[1:40]

R/estimate_infections.R

@@ -101,7 +101,11 @@
 #' )
 #' # set an example reporting delay. In practice this should use an estimate
 #' # from the literature or be estimated from data
-#' reporting_delay <- LogNormal(mean = 2, sd = 1, max = 10)
+#' reporting_delay <- LogNormal(
+#'   meanlog = Fixed(2),
+#'   sdlog = Fixed(1),
+#'   max = 10
+#' )
 #'
 #' # for more examples, see the "estimate_infections examples" vignette
 #' def <- estimate_infections(reported_cases,

R/regional_epinow.R

@@ -79,11 +79,16 @@
 #'   data = cases,
 #'   generation_time = gt_opts(example_generation_time),
 #'   delays = delay_opts(example_incubation_period + example_reporting_delay),
-#'   rt = rt_opts(prior = LogNormal(mean = 2, sd = 0.2)),
-#'   stan = stan_opts(
-#'     samples = 100, warmup = 200
-#'   ),
-#'   verbose = interactive()
+#'   rt = rt_opts(
+#'     prior = LogNormal(
+#'       meanlog = Fixed(2),
+#'       sdlog = Fixed(0.2)
+#'     ),
+#'     stan = stan_opts(
+#'       samples = 100, warmup = 200
+#'     ),
+#'     verbose = interactive()
+#'   )
 #' )
 #' options(old_opts)
 #' }

R/simulate_infections.R

@@ -277,7 +277,12 @@ simulate_infections <- function(estimates, R, initial_infections,
 #' est <- estimate_infections(reported_cases,
 #'   generation_time = generation_time_opts(example_generation_time),
 #'   delays = delay_opts(example_incubation_period + example_reporting_delay),
-#'   rt = rt_opts(prior = LogNormal(mean = 2, sd = 0.1), rw = 7),
+#'   rt = rt_opts(prior = LogNormal(
+#'      meanlog = Fixed(2),
+#'      sdlog = Fixed(0.1)
+#'    ),
+#'    rw = 7
+#'   ),
 #'   obs = obs_opts(scale = Normal(mean = 0.1, sd = 0.01)),
 #'   gp = NULL, horizon = 0
 #' )

vignettes/EpiNow2.Rmd.orig

@@ -50,7 +50,11 @@ If data was not available we could instead specify an informed estimate of the l
 To demonstrate, we choose a lognormal distribution with mean 2, standard deviation 1 and a maximum of 10. *This is just an example and unlikely to apply in any particular use case*.
 
 ```{r}
-reporting_delay <- LogNormal(mean = 2, sd = 1, max = 10)
+reporting_delay <- LogNormal(
+    meanlog = Fixed(2),
+    sdlog = Fixed(1),
+    max = 10
+)
 reporting_delay
 ```
 
@@ -94,7 +98,10 @@ estimates <- epinow(
   data = reported_cases,
   generation_time = gt_opts(example_generation_time),
   delays = delay_opts(example_incubation_period + reporting_delay),
-  rt = rt_opts(prior = LogNormal(mean = 2, sd = 0.2)),
+  rt = rt_opts(prior = LogNormal(
+    meanlog = Fixed(2),
+    sdlog = Fixed(0.2)
+  )),
   stan = stan_opts(cores = 4, control = list(adapt_delta = 0.99)),
   verbose = interactive()
 )
@@ -148,7 +155,13 @@ estimates <- regional_epinow(
   data = reported_cases,
   generation_time = gt_opts(example_generation_time),
   delays = delay_opts(example_incubation_period + reporting_delay),
-  rt = rt_opts(prior = LogNormal(mean = 2, sd = 0.2), rw = 7),
+  rt = rt_opts(
+    prior = LogNormal(
+      meanlog = Fixed(2),
+      sdlog = Fixed(0.2)
+    ),
+    rw = 7
+  ),
   gp = NULL,
   stan = stan_opts(cores = 4, warmup = 250, samples = 1000)
 )

vignettes/epinow.Rmd.orig

@@ -38,9 +38,16 @@ This should be replaced with parameters relevant to the system that is being stu
 library("EpiNow2")
 options(mc.cores = 4)
 reported_cases <- example_confirmed[1:60]
-reporting_delay <- LogNormal(mean = 2, sd = 1, max = 10)
+reporting_delay <- LogNormal(
+  meanlog = Fixed(2),
+  sdlog = Fixed(1),
+  max = 10
+)
 delay <- example_incubation_period + reporting_delay
-rt_prior <- LogNormal(mean = 2, sd = 0.1)
+rt_prior <- LogNormal(
+  meanlog = Fixed(2),
+  sdlog = Fixed(0.1)
+)
 ```
 
 We can then run the `epinow()` function with the same arguments as `estimate_infections()`.

vignettes/estimate_infections_options.Rmd.orig

@@ -73,7 +73,11 @@ For the reporting delay, we use a lognormal distribution with mean of 2 days and
 Note that the mean and standard deviation must be converted to the log scale, which can be done using the `convert_log_logmean()` function.
 
 ```{r reporting_delay}
-reporting_delay <- LogNormal(mean = 2, sd = 1, max = 10)
+reporting_delay <- LogNormal(
+  meanlog = Fixed(2),
+  sdlog = Fixed(1),
+  max = 10
+)
 reporting_delay
 ```
 
@@ -97,7 +101,10 @@ example_generation_time
 Lastly we need to choose a prior for the initial value of the reproduction number. This is assumed by the model to be normally distributed and we can set the mean and the standard deviation. We decide to set the mean to 2 and the standard deviation to 1.
 
 ```{r initial_r}
-rt_prior <- LogNormal(mean = 2, sd = 0.1)
+rt_prior <- LogNormal(
+  meanlog = Fixed(2),
+  sdlog = Fixed(0.1)
+)
 ```
 
 # Running the model
@@ -172,8 +179,8 @@ Here, instead of doing so we assume that we know about truncation with mean of 1
 
 ```{r define_truncation}
 trunc_dist <- LogNormal(
-  mean = Normal(0.5, 0.1),
-  sd = Normal(0.5, 0.1),
+  meanlog = Normal(0.5, 0.1),
+  sdlog = Normal(0.5, 0.1),
   max = 3
 )
 trunc_dist

vignettes/estimate_infections_workflow.Rmd.orig

@@ -79,7 +79,7 @@ In all cases, the distributions given can be *fixed* (i.e. have no uncertainty)
 For example, to define a fixed gamma distribution with mean 3, standard deviation (sd) 1 and maximum value 10, you would write
 
 ```{r}
-fixed_gamma <- Gamma(mean = 3, sd = 1, max = 10)
+fixed_gamma <- Gamma(shape = 3, rate = 1, max = 10)
 fixed_gamma
 ```
 
@@ -93,7 +93,7 @@ If distributions are variable, the values with uncertainty are treated as [prior
 For example, to define a variable gamma distribution where uncertainty in the mean is given by a normal distribution with mean 3 and sd 2, and uncertainty in the standard deviation is given by a normal distribution with mean 1 and sd 0.1, with a maximum value 10, you would write
 
 ```{r}
-uncertain_gamma <- Gamma(mean = Normal(3, 2), sd = Normal(1, 0.1), max = 10)
+uncertain_gamma <- Gamma(shape = Normal(3, 2), rate = Normal(1, 0.1), max = 10)
 uncertain_gamma
 ```
 
@@ -209,7 +209,7 @@ It can be changed using the `rt_opts()` function.
 For example, if the user believes that at the very start of the data the reproduction number was 2, with uncertainty in this belief represented by a standard deviation of 1, they would use
 
 ```{r results = 'hide'}
-rt_prior <- LogNormal(mean =  2, sd = 1)
+rt_prior <- LogNormal(meanlog = Fixed(2), sdlog = Fixed(1))
 rt_opts(prior = rt_prior)
 ```