This is a patch to fix a bug introduced by prediction 0.3.6.
Published by leeper over 6 years ago
This is the first CRAN release of margins. It contains the follow changes since 0.2.0:
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)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)"marginslist"
have been removed. (#58)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."merMod"
models from lme4, though no variance estimation is currently supported.prediction::mean_or_mode()
for use in cplot()
methods.cplot.polr()
now allows the display of "stacked" (cumulative) predicted probabilities. (#49)cplot(draw = "add")
to display predicted probabilities across a third factor variable. (#46)build_datalist()
and seq_range()
functions to the prediction package.cplot.multinom()
method has been added.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)cplot()
method for objects of class "polr"
has been added (#49).extract_marginal_effects()
function has been removed and replaced by marginal_effects()
methods for objects of classes "margins"
and "marginslist"
.prediction()
issue for models of class "svyglm"
. (#47, h/t Carl Ganz)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).get_effect_variances(vce = "bootstrap")
, wherein the variance of the marginal effects was always zero.prediction()
generic and methods into a separate package, prediction, to ease maintainence.print.summary.margins()
method to separate construction of the summary data frame the printing thereof.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)cplot()
data. (#31)dydx.default()
to allow the calculation of various discrete changes rather than only numerical derivatives.data
argument in margins()
and prediction()
to be clearer about what is happening when it is set to missing.marfx
(#31, h/t Jeffrey Arnold)prediction()
methods to, hopefully, reduce memory footprint of model objects. (#26)variances
field in "margins" objects (to lower case), for consistency.dydx()
generic and methods to provide variable-specific marginal effects calculations. (#31)image()
method for "lm", "glm", and "loess" objects, as a flat complement to existing persp()
methods. (#42)prediction()
method for "gls" objects (from MASS::gls()
). (#3)numDeriv::jacobian()
with an internal alternative. (#41)prediction()
method for "ivreg" objects (from AER::ivreg()
). (#3)prediction()
method for "survreg" objects (from survival::survreg()
). (#3)prediction()
method for "polr" objects (from MASS::polr()
). (#3)prediction()
method for "coxph" objects (from survival::coxph()
). (#3)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.prediction()
, marginal_effects()
, cplot()
, and persp()
. No effect variances are currently calculated. (#3)prediction()
method for "nls" objects. (#3)get_effect_variances()
gains a "none" option for the vce
argument, to skip calculation of ME variances.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)x
variables in cplot()
..build_predict_fun()
factory function, as it was no longer needed.marginal_effects()
to use a vectorized approach to simple numerical differentiation. (#36/#37, h/t Vincent Arel-Bundock)margins.plm()
method, which didn't actually work because "plm" does not provide a predict()
method.inst/doc
.atmeans
argument throughout package. (#35)vc
argument to vcov
for consistency with other packages. (#34)build_margins()
now returns columns containing unit-specific standard errors of marginal effects.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.get_marginal_effects()
wherein the method
argument was ignored. This improves performance significantly when using method = "simple"
(the default differentiation method).Published by leeper over 6 years ago
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.
Published by leeper about 8 years ago
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):
plot.margins()
method for mimicking Stata's marginsplot
behavior.persp()
methods for "lm" and "glm" class objects to display 3-dimensional representations of predicted values and marginal effects.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.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).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))
).build_margins()
, thereby improving estimation speed.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.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.