Function pre now allows use of function randomForest (from package of same name) for rule induction though argument randomForest.
Functions singleplot and pairplot now plot observed values for predictor(s) of interest as a rug on the x-axis, and invisibly return the evaluated partial dependence function(s) for further use.
Bug fixed in computation of importances: factors included as linear terms did not contribute to variable importances. Now they do.
Function mi_pre added, to allow for fitting a single prediction rule ensemble on multiply imputed datasets.
Function prune_pre added, to extract optimal penalty values for an ensemble of given size.
Adaptive lasso has been implemented in function pre. Arguments ad_alpha and ad_penalty allow for fitting the final rule ensemble using adaptive lasso. See documentation of function pre and vignette(“relaxed”, “pre”).
Partial dependence plotting functions singleplot and pairplot now also support multinomial and multivariate outcomes.
Vignette added detailing how function pre’s computatation time can be reduced.
Implemented relaxed lasso fits and added vignette on relaxed fits, see vignette(“relaxed”, “pre”).
More informative warning message if no rules could be derived.
Added vignette explaining how to tune parameters of function pre() using package caret.
Added vignette on how to deal with missing values.
Added support for supplying a tibble instead of a data.frame to function pre().
Function importance is now an S3method.
Argument cex.axis of function (method) importance is now passed correctly.
Argument weights of functions pre() and gpe() now correctly passed to rule induction functions.
Added progress bar indicating tree fitting progress.
Argument singleconditions implemented in function pre() (experimental).
Minor changes for compatibility with changed data.frame function in R version 4.0.0.
References to paper in Journal of Statistical Software included.
Bugs fixed in caret_pre_model: tuning of penalty.par.val argument always yielded results for “lambda.1se” only. Results are now correctly returned for “lambda.min” and “lambda.1se”. caret’s varImp() and predictors() not supported (perhaps temporarily), as these would always employ default penalty.par.val of “lambda.1se”.
Bugs fixed in explain().
Added support for sparse rule matrix, which can be invoked through sparse argument in pre(). If sparse = TRUE, memory usage will be reduced and computation speed may be improved for large datasets.
Added function explain(), which provides (graphical) explanations of the ensemble’s predictions at the individual observation level.
Added support for survival responses (i.e., family = “cox”) in pre()
Added summary methods for pre and gpe.
Extended support to all response variable types available in pre() for functions plot(), importance() and cvpre().
plot.pre now allows for specifying separate plotting colors for rules with positive and negative coefficients.
coef and print methods for pre now return descriptions for the intercept (and factor variables), thanks to suggestion by Stephen Milborrow.
Bug fix in pre(): ordered factors no longer yield error. Implemented new argument ‘ordinal’ in pre(), which specifies how ordered factors should be processed.
Bug fix in cvpre(): pclass argument now processed correctly.
Bug fix in cvpre(): previously, SDs insteas of SEs were returned for binary classification. Accurate standard errors are returned now.
Bugs fixed in coef.pre(), print.pre(), plot.pre() and importance() when tree.unbiased = FALSE, thanks to a bug report by Stephen Milborrow.
Function pre() now also supports multinomial and multivariate gaussian response variables.
Function pre() now has argument ‘tree.unbiased’; if set to FALSE, the CART algorithm (as implemented in package ‘rpart’) is employed for rule induction.
Argument ‘maxdepth’ of function pre() allows for specifying varying maximum depth across trees, through specifying a vector of length ntrees, or a random number generating function. See ?maxdepth.sampler for details.
Added dataset ‘carrillo’
By default, a gradient boosting approach is now taken for all response types. That is, partykit::ctree() and a learning rate of .01 is employed by default. Alternatively, glmtree() can be employed for tree induction by sprecifying use.grad = FALSE.
The ‘family’ argument in pre() now takes character strings as well as glm family objects.
Functions pairplot() and interact() now use HCL instead of highly saturated HSV colors as default plotting colors.
Bug fixed in plot.pre: Node directions are now in accordance with rule definition.
Bug fixed in predict.pre: No error printed when response variable is not supplied.
Function gpe() added, which fits general prediction ensembles. By default, it fits an ensemble of rules, linear and hinge functions. Function gpe() allows for specifying custom baselearner generating functions and a custom fitting function for the final model.
Numerous bugs fixed, yielding faster computation times and clearer plots with more customization options.
Added support for count responses. Function pre() now has a ‘family’ argument, which should be set to ‘poisson’ for count outcomes (the ‘family’ argument is set automatically to ‘gaussian’ for numeric response variables and to ‘binomial’ for binary response variables (factors)).
A gradient boosting approach for binary outcomes is applied, by default, substantially reducing computation times. This can be turned off through the ‘use.grad’ argument in function pre().
The default of the ‘learnrate’ argument of function pre() has been changed to .01, by default. Before, it was .01 for continuous outcomes, but 0 for binary outcomes, to reduce computation time. With gradient boosting implemented, computation time is much reduced.
Argument ‘tree.control’ in function pre() allows for passing arguments to partykit tree fitting functions.
Arguments for the cv.glmnet() function are directly passed through better use of ellipsis (…). Most importantly, this means that argument ‘mod.sel.crit’ cannot be used anymore and should be referred to as ‘type.measure’ (which will be directly passed to cv.glmnet). Similarly, ‘thres’ and ‘standardize’ are not explicit arguments of function pre() anymore and can now be directly passed to cv.glmnet() using ellipsis (…).
Better use of sample weights: weights specified with the ‘weights’ argument in pre() are now used as weights in the subsampling procedure, instead of as observation weights in the tree-fitting procedure.
Added corplot() function, which shows the correlation between the baselearners in the ensemble.
Function pairplot() returns a heatmap by default, a 3D or contour plot can also be requested.
Appearance of plot resulting from interaction() improved.
Added print() and plot() method for objects of class pre.
Added support for using functions like factor() and log() in formula statement of function pre(). (thanks to Bill Venables for suggesting this)
Added support for parallel computating in functions pre(), cvpre(), bsnullinteract() and interact().
Winsorizing points used for the linear terms are reported in the description of the base learners returned by coef() and importance(). (Thanks to Rishi Sadhir for suggesting this)
Added README file.
Legend included in plot for interaction test statistics.
Fixed importance() function to allow for selecting final ensemble with different value than ‘lambda.1se’.
Cleaned up all occurrences of set.seed()
Fixed cvpre() function: penalty.par.val argument now included
Many minor bug fixes.
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