1 files changed, 15 insertions, 3 deletions
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index f23e1c7f1f..8d3b4388ae 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -32,6 +32,12 @@ setClass("PipelineModel", representation(model = "jobj"))
 #' @param family Error distribution. "gaussian" -> linear regression, "binomial" -> logistic reg.
 #' @param lambda Regularization parameter
 #' @param alpha Elastic-net mixing parameter (see glmnet's documentation for details)
+#' @param standardize Whether to standardize features before training
+#' @param solver The solver algorithm used for optimization, this can be "l-bfgs", "normal" and
+#'               "auto". "l-bfgs" denotes Limited-memory BFGS which is a limited-memory
+#'               quasi-Newton optimization method. "normal" denotes using Normal Equation as an
+#'               analytical solution to the linear regression problem. The default value is "auto"
+#'               which means that the solver algorithm is selected automatically.
 #' @return a fitted MLlib model
 #' @rdname glm
 #' @export
@@ -79,9 +85,15 @@ setMethod("predict", signature(object = "PipelineModel"),
 #'
 #' Returns the summary of a model produced by glm(), similarly to R's summary().
 #'
-#' @param x A fitted MLlib model
-#' @return a list with a 'coefficient' component, which is the matrix of coefficients. See
-#'         summary.glm for more information.
+#' @param object A fitted MLlib model
+#' @return a list with 'devianceResiduals' and 'coefficients' components for gaussian family
+#'         or a list with 'coefficients' component for binomial family. \cr
+#'         For gaussian family: the 'devianceResiduals' gives the min/max deviance residuals
+#'         of the estimation, the 'coefficients' gives the estimated coefficients and their
+#'         estimated standard errors, t values and p-values. (It only available when model
+#'         fitted by normal solver.) \cr
+#'         For binomial family: the 'coefficients' gives the estimated coefficients.
+#'         See summary.glm for more information. \cr
 #' @rdname summary
 #' @export
 #' @examples