High-Performance Open-Source Archive
prediction archive cascadegapminder in vignettecairo_pdf in vignette, per
CRAN policy.cplot.default() method and modified
documentation of cplot(), image(), and
persp() methods slightly. (#84, h/t Luke Sonnet)cplot() for
generalized linear models, which can generate unexpected confidence
intervals (albeit ones consistent with base R’s behavior). (#92)NAs and errors in
margins() when vce was
"bootstrap" or "simulation" and
variables had a length of 1. (#112)lme4 package can now have variance
estimation via bootstrap and simulation (#105).margins_summary() which provides a
single-function expression of summary(margins(...)). (#94,
h/t Mike DeCrescenzo)persp() related to attempting to take the
mean of a factor variable. (#93, h/t Jared Knowles)print() and summary()
methods related to the release of prediction 0.3.6.gradient_factory() to be more
robust to an expanded set of model classes through the introduction of
an internal function reset_coefs(). A test suite for this
function has been added.margins.default() now attempts to calculate marginal
effect variances in order to, by default, support additional model
classes.margins() now returns attributes “vcov” and “jacobian”
(the latter only when vce = "delta"), which contain the
full variance-covariance matrix for the average marginal effects and
jacobian for the same. This is different behavior from the previous
draft (v0.3.17) because the attributes now always contain a single
matrix; again use the vcov() method rather than accessing
the attribute directly lest it change in the future. This allows
calculation combination of marginal effects, such as the difference
between two AMEs. Some internal functions have been renamed and code
reorganized to make this possible. (#87, h/t Trenton Mize)margins() now contains
the input value passed to the at argument to the function.
New attribute “at_vars” returns a character vector of variables
specified therein.margins() now contains an
added column "_at_number", which specifies which
at combination a row comes from. This may be changed or
removed in the future, but is useful for matching subsets of the data
frame to corresponding entries in the “vcov” and “jacobian”
matrices.margins() now returns an attribute (“vcov”) containing
the variance-covariance matrix for the average marginal effects and a
new vcov.margins() method is provided for extracting it.
Behavior when using at specifications is unspecified and
may change in the future. (#87, h/t Trenton Mize)README.Rmd. (#83)cplot() when xvar was of
class “ordered”. (#77, h/t Francisco Llaneras)plot.margins() when at
contained only one variable. (#78, h/t @cyberbryce)y ~ I(x^2), y ~ x + I(2*x), and those
involving RHS interactions between factors where some cells are not
observed in the data. Added a test suite to cover these cases.
(#82)find_terms_in_model().clean_terms()) that occurred when formulae contained
variables with backticked names that contained spaces. (#80)dydx() now uses the performance-enhancing
prediction::prediction(..., calculate_se = FALSE) setting,
where possible (introduced in prediction 0.2.4)data.table::rbindlist() is used instead of
base::rbind() inside dydx().marginal_effects() and
dydx() gain an as.data.frame argument to
regulate the class of their responses.prediction::prediction() were halved
by stacking data frames used in calculating numerical derivatives
(inside dydx() methods) and then splitting the resulting
predicted value vectors.varslist, to
marginal_effects() and several internal functions that
significantly improves performance. The performance gain is due to
computational cost of identifying terms in model formulae each time
marginal_effects() was called, which occurred repeatedly
(e.g., during variance estimation). By performing this once at the
margins()-level and passing the argument throughout,
margins() is perhaps twice as fast as in versions <=
0.3.9. But, importantly, note that this argument should not be specified
by end users!find_terms_in_model() and clean_terms(),
removing many regex calls with the goal of improving performance.margins() for unweighted
models.print() and summary() now handle
survey-weighted marginal effects.margins() and marginal_effects() gain a
variables argument to request marginal effects for a subset
of variables included in a model. (#65, h/t Vincent Arul-Bundock)margins.merMod(). (#56)cplot.clm() method. (#63, h/t David
Barron)cplot.polr(). (#62, h/t David
Barron)margins.merMod().print() and summary() methods to
using weighted.mean() instead of mean().
(#45)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).persp() methods for “lm” and “glm” class objects
to display 3-dimensional representations of predicted values and
marginal effects.plot.margins() method for mimicking Stata’s
marginsplot behavior.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). Second,
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() rather than symbolic
differentiation. This allows margins() to handle almost any
model that can be specified in R, including models that cannot be
specified in Stata.build_datalist() now checks for
specification of illegal factor levels in at and errors
when these are encountered.
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