Merge pull request #52 from ropensci/issue51

Issue51

R/orsf_R6.R

@@ -2883,6 +2883,7 @@ ObliqueForest <- R6::R6Class(
    oob_data <- data.table(
     n_predictors = seq(n_predictors),
     stat_value = rep(NA_real_, n_predictors),
+    variables_included = vector(mode = 'list', length = n_predictors),
     predictors_included = vector(mode = 'list', length = n_predictors),
     predictor_dropped = rep(NA_character_, n_predictors)
    )
@@ -2915,6 +2916,34 @@ ObliqueForest <- R6::R6Class(
                                      importance_group_factors = TRUE,
                                      write_forest = FALSE)
 
+
+   fctr_key <- lapply(self$get_fctr_info()$keys, function(x) x[-1])
+
+   fctr_key <- data.frame(
+    variable = rep(names(fctr_key), sapply(fctr_key, length)),
+    predictor = unlist(fctr_key),
+    row.names = NULL
+   )
+
+   variable_key <- data.frame(
+    predictor = self$get_names_x(ref_coded = TRUE)
+   )
+
+   if(is_empty(fctr_key)){
+
+    variable_key$variable <- variable_key$predictor
+
+   } else {
+
+    variable_key <- merge(variable_key, fctr_key,
+                          by = 'predictor',
+                          all.x = TRUE)
+
+   }
+
+   variable_key$variable[is.na(variable_key$variable)] <-
+    variable_key$predictor[is.na(variable_key$variable)]
+
    max_progress <- n_predictors - n_predictor_min
    current_progress <- 0
    start_time <- last_time <- Sys.time()
@@ -2963,9 +2992,17 @@ ObliqueForest <- R6::R6Class(
     worst_index <- which.min(cpp_output$importance)
     worst_predictor <- colnames(cpp_args$x)[worst_index]
 
+
+    .variables_included <- with(
+     variable_key,
+     unique(variable[predictor %in% colnames(cpp_args$x)])
+    )
+
+
     oob_data[n_predictors,
              `:=`(n_predictors = n_predictors,
                   stat_value = cpp_output$eval_oobag$stat_values[1,1],
+                  variables_included = .variables_included,
                   predictors_included = colnames(cpp_args$x),
                   predictor_dropped = worst_predictor)]
 

R/orsf_vs.R

@@ -8,11 +8,19 @@
 #' @return a [data.table][data.table::data.table-package] with four columns:
 #'   - *n_predictors*: the number of predictors used
 #'   - *stat_value*: the out-of-bag statistic
+#'   - *variables_included*: the names of the variables included
 #'   - *predictors_included*: the names of the predictors included
 #'   - *predictor_dropped*: the predictor selected to be dropped
 #'
 #' @details
 #'
+#' The difference between `variables_included` and `predictors_included` is
+#'  referent coding. The `variable` would be the name of a factor variable
+#'  in the training data, while the `predictor` would be the name of that
+#'  same factor with the levels of the factor appended. For example, if
+#'  the variable is `diabetes` with `levels = c("no", "yes")`, then the
+#'  variable name is `diabetes` and the predictor name is `diabetes_yes`.
+#'
 #' `tree_seeds` should be specified in `object` so that each successive run
 #'   of `orsf` will be evaluated in the same out-of-bag samples as the initial
 #'   run.