![]() The variable inclusion proportions for the actual data. Names of the variables chosen by the Global SE procedure.Ĭolumn numbers of the variables chosen by the Local procedure.Ĭolumn numbers of the variables chosen by the Global Max procedure.Ĭolumn numbers of the variables chosen by the Global SE procedure. Names of the variables chosen by the Global Max procedure. Names of the variables chosen by the Local procedure. Invisibly, returns a list with the following components: (2013) for a complete description of the procedures outlined above as well as the corresponding vignette for a brief summary with examples. Note that making this parameter too large will prevent plotting and the plot function in R will throw an error. Higher values allow for more space if the crossed covariate names are long. ![]() The scale of this parameter is the same as set with par(mar = c(.)) in R. Number of variables (in order of decreasing variable inclusion proportion) to be plotted.Ī display parameter that adjusts the bottom margin of the graph if labels are clipped. If TRUE, a plot showing which variables are selected by each of the procedures is generated. Note that this number is used to estimate both the “true” and “null” variable inclusion proportions. Investigate_var_importance, a small number of trees should be used to force variables to compete for entry into the model. Number of trees to use in the variable selection procedure. Number of permutations of the response to be made to generate the “null” permutation distribution. Number of replicates to over over to for the BART model's variable inclusion proportions. Plot = TRUE, num_var_plot = Inf, bottom_margin = 10) Num_trees_for_permute = 20, alpha = 0.05, Num_reps_for_avg = 10, num_permute_samples = 100, Usage var_selection_by_permute(bart_machine, Performs variable selection using the three thresholding methods introduced in Bleich et al. ![]() Perform Variable Selection using Three Threshold-based Procedures ![]()
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