The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
Set R_NO_REMAP per changes in CRAN
policies.
Updated package documentation since
@docType "package" is deprecated.
Removed unnecessary get_training_data.Rd file and
cleaned up arguments.
Update citation file to use c() instead of
personList() and bibentry() instead of
citEntry().
partial()’s progress argument now only accepts a
logical and defaults to FALSE.New (experimental) function exemplar() for
constructing an “exemplar” record from a data frame or matrix-like
object. See ?pdp::exemplar for details (#91).
partial() gained a new (experimental) feature via
the new approx argument. If approx = TRUE,
then partial() will compute predictions across the
predictors specified in pred.var while holding the other
predictors constant (a “poor man’s partial dependence” function as
Stephen Milborrow, the author of plotmo, puts it).
See ?pdp::partial for details.
Bumped the R dependency to R (>= 3.6.0) to support the use of
grDevices::hcl.colors() in
plotPartial().
Function grid.arrange() and the forward pipe
operator %>% are no longer automatically imported from
packages gridExtra and magrittr,
respectively; users are encouraged to load them manually if
needed.
Removed the palette and alpha arguments
from plotPartial() and autoplot(); the latter
just got absorbed into the ... argument. By default,
plotPartial()’s col.regions argument now
corresponds to grDevices::hcl.colors(100), which defaults
to the same viridis color palette as before, just without the
dependency.
topPredictors() is now deprecated and will be
removed in the next update. Users are advised to use the vip package
instead.
Added support for gradient boosted Cox proportional hazards models in gbm.
Removed dependency on the retired plyr package by
relying directly on the foreach package.
Consequently, ICE curves (ice = TRUE) are now slightly
faster to compute (since the code refactoring avoids having to
post-process ICE data from wide to long format) and the corresponding
progress bar (progress = "text") is more honest.
partial() function only
supports a simple text-based progress bar
(progress = "text"), but more options will possibly be
added later.Removed dependency on viridis;
consequently, to keep the ‘viridis’ color palettes in
autoplot(), this required bumping the ggplot2 dependency
to version 3.0.0, as well as some other code tweaks under the hood (#106).
Removed dependency on mgcv by switching to
an internal C implementation of mgcv’s
in.out() function (#107). (This is
used behind the scenes whenever partial() is called with
chull = TRUE.)
"partial" is now a proper subclass of
"data.frame" (#104); thanks
to @RoelVerbelen
for pointing this out.
Fixed a bug where rug = TRUE would not work properly
for xgboost models whenever calling
partial() with plot = TRUE.
Fixed a bug in partial() where the cats
argument was never actually passed to pred_grid() (#86).
Fixed a bug in partial() for "gbm"
objects when recursive = TRUE that caused factors
(including ordered factors) to be coerced to characters.
Switched from Travis-CI to GitHub Actions for continuous integration.
Refactored code for easier maintenance.
Switched to tinytest framework and increased test coverage (#84).
The internal function get_training_data(), which is
used to (attempt to) extract a fitted model’s training data whenever
train is not specified, is (hopefully) a bit more flexible
and robust in certain special cases(#90).
Minor bug fixes in plotting functions (i.e.,
autoplot() and plotPartial()).
Training data that inherits from class "tibble" is
still not officially supported, but shouldn’t cause as many errors from
this point on.
Using autoplot() with a factor followed by numeric
in pred.var no longer seems to be an issue (#79).
Added support for e1071::naiveBayes(), an
implementation of the standard naive Bayes classifier (#42).
Fixed a bug in plotPartial() that caused the
col.regions argument to have no effect when
levelplot = FALSE (#58).
Fixed a bug with categorical variables in gbm models
when recursive = TRUE (#63).
More informative progress bars (with estimated time to
completion!!) powered by the progress
package. To use, simply call partial() with the option
progress = "progress" (#66).
Added ORCiD ID to the author field in the
DESCRIPTION file.
Way cooler logo?
partial() gained several new plotting options:
plot.engine, which controls the plotting engine used
whenever plot = TRUE (current options include
"lattice" (the default) and "ggplot2" (#71).
The arguments to autoplot() and
plotPartial() are now more consistent with each
other.
The names of (most) helper functions have changed from lowerCamelCase to snake_case.
partial() now works (better) with tibbles (#59).
partial() now treats "xgb.Booster"
objects with objective = "reg:logistc" as regression (#68).
Removed use of ggplot2::aes_string() in
autoplot() (which is soft deprecated as of
ggplot2 version 3.0.0) (#73).
Properly registered native routines and disabled symbol search.
Fixed a bug for gbm models using the multinomial
distribution.
Refactored code to improve structure.
partial() gained three new options:
inv.link (experimental), ice, and
center. The latter two have to do with constructing
individual conditional expectation (ICE) curves and centered ICE (c-ICE)
curves. The inv.link option is for transforming predictions
from models that can use non-Gaussian distributions (e.g.,
glm, gbm, and xgboost). Note that
these options were added for convenience and the same results (plus much
more) can still be obtained using the flexible pred.fun
argument. (#36).
plotPartial() gained five new options:
center, plot.pdp, pdp.col,
pdp.lwd, and pdp.lty; see
?plotPartial for details.
Fixed default y-axis label for autoplot() with two
numeric predictors (#48).
Added CITATION file.
Better support for neural networks from the nnet
package.
Fixed a bug for nnet::multinom() models with binary
response.
Fixed minor pandoc conversion issue with
README.md.
Added subdirectory called tools to hold figures for
README.md.
Added support for MASS::lda(),
MASS::qda(), and mda::mars().
New arguments quantiles, probs, and
trim.outliers in partial. These arguments make
it easier to construct PDPs over the relevant range of a numeric
predictor without having to specify pred.grid, especially
when outliers are present in the predictors (which can distort the
plotted relationship).
The train argument can now accept matrices; in
particular, object of class "matrix" or
"dgCMatrix". This is useful, for example, when working with
XGBoost models (i.e., objects of class
"xgb.Booster").
New logical argument prob indicating whether or not
partial dependence values for classification problems should be returned
on the original probability scale, rather than the centered logit;
details for the centered logit can be found on page 370 in the second
edition of The Elements of Statistical Learning.
Fixed some typos in NEWS.md.
New function autoplot for automatically creating
ggplot2 graphics from "partial"
objects.
partial() is now much faster with "gbm"
object due to a call to gbm::plot.gbm() whenever
pred.grid is not explicitly given by the user.
(gbm::plot.gbm() exploits a computational shortcut that
does not involve any passes over the training data.)
New (experimental) function topPredictors() for
extracting the names of the most “important” predictors. This should
make it one step easier (in most cases) to construct PDPs for the most
“important”” features in a fitted model.
A new argument, pred.fun, allows the user to supply
their own prediction function. Hence, it is possible to obtain PDPs
based on the median, rather than the mean. It is also possible to obtain
PDPs for classification problems on the probability scale. See
?partial for examples.
Minor bug fixes and documentation tweaks.
The ... argument in the call to
partial() now refers to additional arguments to be passed
onto stats::predict() rather than
plyr::aaply(). For example, using partial()
with "gbm" objects will require specification of
n.trees which can now simply be passed to
partial() via the ... argument.
Added the following arguments to partial():
progress (plyr-based progress bars),
parallel (plyr/foreach-based
parallel execution), and paropts (list of additional
arguments passed onto foreach when
parallel = TRUE).
Various bug fixes.
partial() now throws an informative error message
when the pred.grid argument refers to predictors not in the
original training data.
The column name for the predicted value has been changed from
"y" to "yhat".
randomForest is no longer imported.
Added support for the caret package (i.e., objects
of class "train").
Added example data sets: boston (corrected Boston
housing data) and pima (corrected Pima Indians diabetes
data).
Fixed error that sometimes occurred when
chull = TRUE causing the convex hull to not be
computed.
Refactored plotPartial() to be more
modular.
Added gbm support for most
non-"binomial" families`.
randomForest is now imported.
Added examples.
partial() now makes sure each column of
pred.grid has the correct class, levels, etc.
partial() gained a new option,
levelplot, which defaults to TRUE. The
original option, contour, has changed and now specifies
whether or not to add contour lines whenever
levelplot = TRUE.
Fixed a number of URLs.
More thorough documentation.
Fixed a couple of URLs and typos.
Added more thorough documentation.
Added support for C5.0, Cubist, nonlinear least squares, and XGBoost models.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.