Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/11447#discussion_r54573876
--- Diff: R/pkg/R/mllib.R ---
@@ -61,6 +61,34 @@ setMethod("glm", signature(formula = "formula", family =
"ANY", data = "DataFram
return(new("PipelineModel", model = model))
})
+#' Fit an accelerated failure time (AFT) survival regression model.
+#'
+#' Fit an accelerated failure time (AFT) survival regression model,
similarly to R's survreg().
+#'
+#' @param formula A symbolic description of the model to be fitted.
Currently only a few formula
+#' operators are supported, including '~', ':', '+', and
'-'.
+#' @param data DataFrame for training.
+#' @return a fitted MLlib model
+#' @rdname survreg
+#' @export
+#' @examples
+#'\dontrun{
+#' sc <- sparkR.init()
+#' sqlContext <- sparkRSQL.init(sc)
+#' library(survival)
+#' data(ovarian)
+#' df <- createDataFrame(sqlContext, ovarian)
+#' model <- survreg(Surv(futime, fustat) ~ ecog_ps + rx, df)
+#' summary(model)
+#'}
+setMethod("survreg", signature(formula = "formula", data = "DataFrame"),
--- End diff --
We only support "weibull" distribution in ```AFTSurvivalRegression```, so
we don't need arguments ```dist``` like R's ```survreg``` until we supporting
more distributions.
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