remove example due to mlr3proba not being in CRAN

R/FilterUnivariateCox.R

@@ -23,45 +23,10 @@
 #' @template seealso_filter
 #' @export
 #' @examples
-#' if (mlr3misc::require_namespaces(c("mlr3", "mlr3proba", "mlr3pipelines"), quietly = TRUE)) {
-#'   library(mlr3)
-#'   library(mlr3proba)
-#'   library(mlr3pipelines)
 #'
-#'   # encode `sex` (two-level factor)
-#'   task = tsk("rats")
-#'   enc = po("encode", method = "treatment")
-#'   task = enc$train(list(task))[[1L]]
+#' filter = flt("univariate_cox")
+#' filter
 #'
-#'   # simple filter use
-#'   filter = flt("univariate_cox")
-#'   filter$calculate(task)
-#'   as.data.table(filter)
-#'
-#'   # transform to p-value
-#'   10^(-filter$scores)
-#'
-#'   # Use filter in a learner pipeline
-#'   # Note: `filter.cutoff` is selected randomly and should be tuned.
-#'   # The significance level of `0.05` serves as a conventional threshold.
-#'   # The filter returns the `-log10`-transformed scores so we transform
-#'   # the cutoff as well:
-#'   cutoff = -log10(0.05) # ~1.3
-#'
-#'   graph =
-#'     po("filter", filter = flt("univariate_cox"), filter.cutoff = cutoff) %>>%
-#'     po("learner", lrn("surv.coxph"))
-#'   learner = as_learner(graph)
-#'
-#'   learner$train(task)
-#'
-#'   # univariate cox filter scores
-#'   learner$model$surv.univariate_cox$scores
-#'
-#'   # only two features had a score larger than the specified `cutoff` and
-#'   # were used to train the CoxPH model
-#'   learner$model$surv.coxph$train_task$feature_names
-#' }
 FilterUnivariateCox = R6Class("FilterUnivariateCox",
   inherit = Filter,
   public = list(