allow only numeric features

Author: bblodfon

R/FilterUnivariateCox.R

@@ -14,15 +14,17 @@
 #' significance of the feature's impact on survival. The filter value is
 #' `-log10(p)` where `p` is the \eqn{p}-value. This transformation is necessary
 #' to ensure numerical stability for very small \eqn{p}-values. Also higher
-#' values denote more important features.
+#' values denote more important features. The filter works only for numeric
+#' features so please ensure that factor variables are properly encoded, e.g.
+#' using [PipeOpEncode][mlr3pipelines::PipeOpEncode].
 #'
 #' @family Filter
 #' @include Filter.R
 #' @template seealso_filter
 #' @export
 #' @examples
 #' if (requireNamespace("mlr3proba")) {
-#'   task = tsk("rats")
+#'   task = tsk("rats")$select(c("rx","litter"))
 #'   filter = flt("univariatecox")
 #'   filter$calculate(task)
 #'   as.data.table(filter)
@@ -31,6 +33,9 @@
 #' if (mlr3misc::require_namespaces(c("mlr3pipelines", "mlr3proba"), quietly = TRUE)) {
 #'   library("mlr3pipelines")
 #'   task = tsk("rats")
+#'   # encode `sex` which is a two-level factor
+#'   enc = po("encode", method = "treatment")
+#'   task = enc$train(list(task))[[1L]]
 #'
 #'   # Note: `filter.cutoff` is selected randomly and should be tuned.
 #'   # The significance level of `0.05` serves as a conventional threshold.
@@ -61,7 +66,7 @@ FilterUnivariateCox = R6Class("FilterUnivariateCox",
         id = "surv.univariatecox",
         packages = c("mlr3proba"),
         param_set = ps(),
-        feature_types = c("integer", "numeric", "factor"),
+        feature_types = c("integer", "numeric"),
         task_types = "surv",
         label = "Univariate Cox Survival Score",
         man = "mlr3filters::mlr_filters_univariatecox"