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author | Yanbo Liang <ybliang8@gmail.com> | 2015-11-18 13:30:29 -0800 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-11-18 13:30:29 -0800 |
commit | e222d758499ad2609046cc1a2cc8afb45c5bccbb (patch) | |
tree | a378be289b9c80cf81975e419ed6ffb4b229e53e /R/pkg | |
parent | e391abdf2cb6098a35347bd123b815ee9ac5b689 (diff) | |
download | spark-e222d758499ad2609046cc1a2cc8afb45c5bccbb.tar.gz spark-e222d758499ad2609046cc1a2cc8afb45c5bccbb.tar.bz2 spark-e222d758499ad2609046cc1a2cc8afb45c5bccbb.zip |
[SPARK-11684][R][ML][DOC] Update SparkR glm API doc, user guide and example codes
This PR includes:
* Update SparkR:::glm, SparkR:::summary API docs.
* Update SparkR machine learning user guide and example codes to show:
* supporting feature interaction in R formula.
* summary for gaussian GLM model.
* coefficients for binomial GLM model.
mengxr
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #9727 from yanboliang/spark-11684.
Diffstat (limited to 'R/pkg')
-rw-r--r-- | R/pkg/R/mllib.R | 18 |
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 |