Package index • ggRandomForests

All functions

calc_auc(): Area Under the ROC Curve calculator

calc_roc(<rfsrc>): Receiver Operator Characteristic calculator

ggRandomForests-package: ggRandomForests: Visually Exploring Random Forests

gg_error(): randomForest error rate data object

gg_partial(): Split partial lots into continuous or categorical datasets

gg_partial_rfsrc(): Split partial lots into continuous or categorical datasets

gg_partialpro(): Split partial lots into continuous or categorical datasets

gg_rfsrc(<rfsrc>): Predicted response data object

gg_roc(<rfsrc>): ROC (Receiver operator curve) data from a classification random forest.

gg_survival(): Nonparametric survival estimates.

gg_variable(): Marginal variable dependence data object.

gg_vimp(): Variable Importance (VIMP) data object

kaplan(): nonparametric Kaplan-Meier estimates

nelson(): nonparametric Nelson-Aalen estimates

plot(<gg_error>): Plot a gg_error object

plot(<gg_rfsrc>): Predicted response plot from a gg_rfsrc object.

plot(<gg_roc>): ROC plot generic function for a gg_roc object.

plot(<gg_survival>): Plot a gg_survival object.

plot(<gg_variable>): Plot a gg_variable object,

plot(<gg_vimp>): Plot a gg_vimp object, extracted variable importance of a rfsrc object

quantile_pts(): Find points evenly distributed along the vectors values.

r_data_types(): r_data_types infers correct data classes for each column in a data.frame

shift(): lead function to shift by one (or more).

surv_partial.rfsrc(): Calculate survival curve partial plot.

varpro_feature_names(): varpro one hot encodes features, so we need to get the "raw" original variable names. This loops through the variable names not in the original dataset, and cuts one character off the end until we find the variable name in the original data.