CRAN Patch

This is a patch to fix a bug introduced by prediction 0.3.6.

Pre-release for 0.4.0

This release is intended to provide a CRAN version that fixes some long-running issues in advance of 0.4.0, which is coming soon. Full details of changes since 0.3.0 will be described in that release.

Stable Release

This is the first CRAN release of margins. It contains the follow changes since 0.2.0:

0.3.0

• Significantly modified the data structure returned by margins(). It now returns a data frame with an added at attribute, specifying the names of the variables that have been fixed by build_datalist(). (#58)
• Renamed marginal effects, variance, and standard error columns returned by margins(). Marginal effects columns are prefixed by dydx_. Variances of the average marginal effect are stored (repeatedly, across observations) in new Var_dydx_ columns. Unit-specific standard errors, if requested, are stored as SE_dydx_ columns. (#58)
• summary.margins() now returns a single data frame of marginal effect estimates. Column names have also changed to avoid use of special characters (thus making it easier to use column names in plotting with, for example, ggplot2). Row-order can be controlled by the by_factor attribute, which by default sorts the data frame by the factor/term. If set to by_factor = FALSE, the data frame is sorted by the at variables. This behavior cascades into the print.summary.margins() method. (#58)
• print.margins() now presents (but does not return) effect estimates as a condensed data frame with some auxiliary information. Its behavior when using at is improved and tidied. (#58)
• build_margins() is no longer exported. Arguments used to control its behavior have been exposed in margins() methods.
• plot.margins() now displays marginal effects across each level of at. (#58)
• build_margins() and thus margins() no longer returns the original data twice (a bug introduced by change in behavior of prediction()). (#57)
• All methods for objects of class "marginslist" have been removed. (#58)
• The at argument in plot.margins() has been renamed to pos, to avoid ambiguity with at as used elsewhere in the package.
• persp() and image() methods gain a dx argument (akin to that in cplot()) to allow visualization of marginal effects of a variable across levels of two other variables. The default behavior remains unchanged.
• Cleaned up documentation and add some examples.

margins 0.2.26

• Added support for "merMod" models from lme4, though no variance estimation is currently supported.
• Imported prediction::mean_or_mode() for use in cplot() methods.

margins 0.2.25

• cplot.polr() now allows the display of "stacked" (cumulative) predicted probabilities. (#49)
• Added an example of cplot(draw = "add") to display predicted probabilities across a third factor variable. (#46)
• Moved the build_datalist() and seq_range() functions to the prediction package.
• A tentative cplot.multinom() method has been added.

margins 0.2.24

• The internal code of cplot.lm() has been refactored so that the actual plotting code now relies in non-exported utility functions, which can be used in other methods. This should make it easier to maintain existing methods and add new ones. (#49)
• A new cplot() method for objects of class "polr" has been added (#49).

margins 0.2.23

• The extract_marginal_effects() function has been removed and replaced by marginal_effects() methods for objects of classes "margins" and "marginslist".
• Added a dependency on prediction v.0.1.3 and, implicitly, an enhances suggestion of survey v3.31-5 to resolve an underlying prediction() issue for models of class "svyglm". (#47, h/t Carl Ganz)

margins 0.2.20

• A warning is now issued when a model uses weights, indicating that they are ignored. (#4)
• Various errors and warnings that occurred when applying margins() to a model with weights have been fixed.
• cplot() now issues an error when attempting to display the effects of a factor (with > 2 levels).

margins 0.2.20

• Fixed a bug in get_effect_variances(vce = "bootstrap"), wherein the variance of the marginal effects was always zero.

margins 0.2.20

• Factored the prediction() generic and methods into a separate package, prediction, to ease maintainence.
• Added a print.summary.margins() method to separate construction of the summary data frame the printing thereof.
• The "Technical Details" vignette now describes the package functionality and computational approach in near-complete detail.

margins 0.2.19

• Plotting functions cplot(), persp(), and image() gain a vcov argumetn to pass to `build_margins(). (#43)
• cplot() now allows for the display of multiple conditional relationships by setting draw = "add". (#32)
• The package Introduction vignette has improved examples, including ggplot2 examples using cplot() data. (#31)

margins 0.2.18

• Added support in dydx.default() to allow the calculation of various discrete changes rather than only numerical derivatives.

margins 0.2.17

• Fixes to handling of factors and ordered variables converted within formulae. (#38)
• Reconfigured the data argument in margins() and prediction() to be clearer about what is happening when it is set to missing.

margins 0.2.16

• Switched to using a more reliable "central difference" numerical differentiation and updated the calculation of the step size to follow marfx (#31, h/t Jeffrey Arnold)
• Added some functionality prediction() methods to, hopefully, reduce memory footprint of model objects. (#26)
• Changed the capitalization of the variances field in "margins" objects (to lower case), for consistency.
• Fixed some small errors in documentation and improved width of examples.

margins 0.2.15

• Expose previously internal dydx() generic and methods to provide variable-specific marginal effects calculations. (#31)
• Added example dataset from marfx package. (#31)

margins 0.2.13

• Added support for calculating marginal effects of logical terms, treating them as factors. (#31)

margins 0.2.12

• Added an image() method for "lm", "glm", and "loess" objects, as a flat complement to existing persp() methods. (#42)

margins 0.2.11

• Added a prediction() method for "gls" objects (from MASS::gls()). (#3)

margins 0.2.10

• Replaced numDeriv::jacobian() with an internal alternative. (#41)

margins 0.2.8

• Added a prediction() method for "ivreg" objects (from AER::ivreg()). (#3)
• Added a prediction() method for "survreg" objects (from survival::survreg()). (#3)

margins 0.2.7

• Added a prediction() method for "polr" objects (from MASS::polr()). (#3)
• Added a prediction() method for "coxph" objects (from survival::coxph()). (#3)

margins 0.2.7

• marginal_effects() and prediction() are now S3 generics, with methods for "lm" and "glm" objects, improving extensability. (#39, #40)
• prediction() returns a new class ("prediction") and gains a print() method.
• Added preliminary support for "loess" objects, including methods for prediction(), marginal_effects(), cplot(), and persp(). No effect variances are currently calculated. (#3)
• Added a prediction() method for "nls" objects. (#3)
• Internal function get_effect_variances() gains a "none" option for the vce argument, to skip calculation of ME variances.

margins 0.2.7

• marginal_effects() issues a warning (rather than fails) when trying to extract the marginal effect of a factor variable that was coerced to numeric in a model formula via I(). (#38)

margins 0.2.5

• Added better support for factor x variables in cplot().
• Added (rudimentary) tests of variance methods. (#21)
• Removed .build_predict_fun() factory function, as it was no longer needed.
• Fix vignettes so package can be built with them. (#16)

margins 0.2.4

• Modified marginal_effects() to use a vectorized approach to simple numerical differentiation. (#36/#37, h/t Vincent Arel-Bundock)
• Removed margins.plm() method, which didn't actually work because "plm" does not provide a predict() method.
• Updated Stata/R comparison documents included in inst/doc.
• Expanded tests of unit-specific variances. (#21)

margins 0.2.3

• Added a logical argument to enable/disable calculation of unit-specific marginal effect variances and set it to FALSE by default. (#36, h/t Vincent Arel-Bundock)

margins 0.2.2

• Removed support for "marginal effects at means" (MEMs) and the atmeans argument throughout package. (#35)
• Renamed the vc argument to vcov for consistency with other packages. (#34)

margins 0.2.1

• build_margins() now returns columns containing unit-specific standard errors of marginal effects.
• Added a vc argument to build_margins() to allow the passing of arbitrary variance-covariance matrices. (#16, h/t Alex Coppock & Gijs Schumacher)
• cplot() now draws confidence intervals for "effect" plots.
• Fixed a bug in get_marginal_effects() wherein the method argument was ignored. This improves performance significantly when using method = "simple" (the default differentiation method).

Beta release

This is a beta release of margins. Earlier versions (available in the git commit history on GitHub) were largely a "proof of concept" effort. The current release (v0.2.0) could benefit from improvements, particularly with regard to speed of variance estimation, but represents a first draft that is for the most part complete and represents a first full draft of the package API.

Some notes about this release (compared to previous "alpha" versions):

Plotting

• Added plot.margins() method for mimicking Stata's marginsplot behavior.
• Added persp() methods for "lm" and "glm" class objects to display 3-dimensional representations of predicted values and marginal effects.
• Added cplot() generic and methods for "lm" and "glm" class objects to display conditional predictions and conditional marginal effects in the style of the interplot and plotMElm packages.

Estimation and statistical details

• Implemented estimation of "discrete change" representations of marginal effects of factor variables in models, ala Stata's default settings. It is currently not possible to do - as in Stata - "continuous" estimation (i.e., partial derivatives) of factors. If you want this, enter a factor variable into the model as a numeric instead of as a factor.
• Added various variance estimation procedures for marginal effects: delta method (the default), bootstrap, and simulation (ala Clarify).
• Fixed estimation of marginal effect variances for generalized linear models, so that they are correct on both "link" and "response" scales.
• Exposed two internal marginal effect estimation functions:
  • build_margins() is called by margins() methods (perhaps repeatedly) and actually assembles a "margins" object from a model and data. It is never necessary to call this directly, but may be useful for very simple marginal effect estimation procedures (i.e., using original data with no at specification).
  • marginal_effects() is the very low level function that differentiates a model with respect to some input data (or calculate discrete changes in the outcome with respect to factor variables). This is the fastest way to obtain marginal effects without the overhead of creating a "margins" object (for which variance estimation is fairly time-consuming).

Computational details

• Re-implemented marginal effects estimation using numeric derivatives provided by numDeriv::grad() and numDeriv::jacobian() rather than symbolic differentiation (via D() and deriv()). This allows margins() to handle almost any model that can be specified in R, including models that cannot be specified in Stata (e.g., y ~ x + I(log(x))).
• Used the compiler package to byte compile the internal prediction and gradient functions used by build_margins(), thereby improving estimation speed.
• The internal build_datalist() now checks for specification of illegal factor levels in at and errors when these are encountered, as well as issues warnings when requesting values outside of the observed range of numeric variables.

Thanks and inspiration

Some thanks are due to Martin Bisgaard, Justin Esarey and Jane Lawrence Sumner, Christopher Gandrud, Matt Golder, William Greene, Frederik Hjorth, Yue Hu and Frederick Solt, Jay Kahn, Sharyn O'Halloran, Carlisle Rainey, Måns Söderbom, Kim Sønderskov, and StataCorp for statistical help, inspiration, helpful discussions, and/or previous programming efforts.