diff options
Diffstat (limited to 'R/pkg/R/mllib.R')
| -rw-r--r-- | R/pkg/R/mllib.R | 24 |
1 files changed, 24 insertions, 0 deletions
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R index 2127daee18..d6ff2aa22d 100644 --- a/R/pkg/R/mllib.R +++ b/R/pkg/R/mllib.R @@ -29,24 +29,28 @@ #' #' @param jobj a Java object reference to the backing Scala GeneralizedLinearRegressionWrapper #' @export +#' @note GeneralizedLinearRegressionModel since 2.0.0 setClass("GeneralizedLinearRegressionModel", representation(jobj = "jobj")) #' S4 class that represents a NaiveBayesModel #' #' @param jobj a Java object reference to the backing Scala NaiveBayesWrapper #' @export +#' @note NaiveBayesModel since 2.0.0 setClass("NaiveBayesModel", representation(jobj = "jobj")) #' S4 class that represents a AFTSurvivalRegressionModel #' #' @param jobj a Java object reference to the backing Scala AFTSurvivalRegressionWrapper #' @export +#' @note AFTSurvivalRegressionModel since 2.0.0 setClass("AFTSurvivalRegressionModel", representation(jobj = "jobj")) #' S4 class that represents a KMeansModel #' #' @param jobj a Java object reference to the backing Scala KMeansModel #' @export +#' @note KMeansModel since 2.0.0 setClass("KMeansModel", representation(jobj = "jobj")) #' Fits a generalized linear model @@ -73,6 +77,7 @@ setClass("KMeansModel", representation(jobj = "jobj")) #' model <- spark.glm(df, Sepal_Length ~ Sepal_Width, family="gaussian") #' summary(model) #' } +#' @note spark.glm since 2.0.0 setMethod( "spark.glm", signature(data = "SparkDataFrame", formula = "formula"), @@ -120,6 +125,7 @@ setMethod( #' model <- glm(Sepal_Length ~ Sepal_Width, df, family="gaussian") #' summary(model) #' } +#' @note glm since 1.5.0 setMethod("glm", signature(formula = "formula", family = "ANY", data = "SparkDataFrame"), function(formula, family = gaussian, data, epsilon = 1e-06, maxit = 25) { spark.glm(data, formula, family, epsilon, maxit) @@ -138,6 +144,7 @@ setMethod("glm", signature(formula = "formula", family = "ANY", data = "SparkDat #' model <- glm(y ~ x, trainingData) #' summary(model) #' } +#' @note summary(GeneralizedLinearRegressionModel) since 2.0.0 setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"), function(object, ...) { jobj <- object@jobj @@ -173,6 +180,7 @@ setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"), #' @rdname print #' @name print.summary.GeneralizedLinearRegressionModel #' @export +#' @note print.summary.GeneralizedLinearRegressionModel since 2.0.0 print.summary.GeneralizedLinearRegressionModel <- function(x, ...) { if (x$is.loaded) { cat("\nSaved-loaded model does not support output 'Deviance Residuals'.\n") @@ -215,6 +223,7 @@ print.summary.GeneralizedLinearRegressionModel <- function(x, ...) { #' predicted <- predict(model, testData) #' showDF(predicted) #' } +#' @note predict(GeneralizedLinearRegressionModel) since 1.5.0 setMethod("predict", signature(object = "GeneralizedLinearRegressionModel"), function(object, newData) { return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf))) @@ -236,6 +245,7 @@ setMethod("predict", signature(object = "GeneralizedLinearRegressionModel"), #' predicted <- predict(model, testData) #' showDF(predicted) #'} +#' @note predict(NaiveBayesModel) since 2.0.0 setMethod("predict", signature(object = "NaiveBayesModel"), function(object, newData) { return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf))) @@ -256,6 +266,7 @@ setMethod("predict", signature(object = "NaiveBayesModel"), #' model <- spark.naiveBayes(trainingData, y ~ x) #' summary(model) #'} +#' @note summary(NaiveBayesModel) since 2.0.0 setMethod("summary", signature(object = "NaiveBayesModel"), function(object, ...) { jobj <- object@jobj @@ -289,6 +300,7 @@ setMethod("summary", signature(object = "NaiveBayesModel"), #' \dontrun{ #' model <- spark.kmeans(data, ~ ., k=2, initMode="random") #' } +#' @note spark.kmeans since 2.0.0 setMethod("spark.kmeans", signature(data = "SparkDataFrame", formula = "formula"), function(data, formula, k, maxIter = 10, initMode = c("random", "k-means||")) { formula <- paste(deparse(formula), collapse = "") @@ -313,6 +325,7 @@ setMethod("spark.kmeans", signature(data = "SparkDataFrame", formula = "formula" #' fitted.model <- fitted(model) #' showDF(fitted.model) #'} +#' @note fitted since 2.0.0 setMethod("fitted", signature(object = "KMeansModel"), function(object, method = c("centers", "classes"), ...) { method <- match.arg(method) @@ -339,6 +352,7 @@ setMethod("fitted", signature(object = "KMeansModel"), #' model <- spark.kmeans(trainingData, ~ ., 2) #' summary(model) #' } +#' @note summary(KMeansModel) since 2.0.0 setMethod("summary", signature(object = "KMeansModel"), function(object, ...) { jobj <- object@jobj @@ -374,6 +388,7 @@ setMethod("summary", signature(object = "KMeansModel"), #' predicted <- predict(model, testData) #' showDF(predicted) #' } +#' @note predict(KMeansModel) since 2.0.0 setMethod("predict", signature(object = "KMeansModel"), function(object, newData) { return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf))) @@ -396,6 +411,7 @@ setMethod("predict", signature(object = "KMeansModel"), #' df <- createDataFrame(infert) #' model <- spark.naiveBayes(df, education ~ ., laplace = 0) #'} +#' @note spark.naiveBayes since 2.0.0 setMethod("spark.naiveBayes", signature(data = "SparkDataFrame", formula = "formula"), function(data, formula, laplace = 0, ...) { formula <- paste(deparse(formula), collapse = "") @@ -423,6 +439,7 @@ setMethod("spark.naiveBayes", signature(data = "SparkDataFrame", formula = "form #' path <- "path/to/model" #' write.ml(model, path) #' } +#' @note write.ml(NaiveBayesModel, character) since 2.0.0 setMethod("write.ml", signature(object = "NaiveBayesModel", path = "character"), function(object, path, overwrite = FALSE) { writer <- callJMethod(object@jobj, "write") @@ -450,6 +467,7 @@ setMethod("write.ml", signature(object = "NaiveBayesModel", path = "character"), #' path <- "path/to/model" #' write.ml(model, path) #' } +#' @note write.ml(AFTSurvivalRegressionModel, character) since 2.0.0 setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "character"), function(object, path, overwrite = FALSE) { writer <- callJMethod(object@jobj, "write") @@ -477,6 +495,7 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c #' path <- "path/to/model" #' write.ml(model, path) #' } +#' @note write.ml(GeneralizedLinearRegressionModel, character) since 2.0.0 setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel", path = "character"), function(object, path, overwrite = FALSE) { writer <- callJMethod(object@jobj, "write") @@ -504,6 +523,7 @@ setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel", pat #' path <- "path/to/model" #' write.ml(model, path) #' } +#' @note write.ml(KMeansModel, character) since 2.0.0 setMethod("write.ml", signature(object = "KMeansModel", path = "character"), function(object, path, overwrite = FALSE) { writer <- callJMethod(object@jobj, "write") @@ -525,6 +545,7 @@ setMethod("write.ml", signature(object = "KMeansModel", path = "character"), #' path <- "path/to/model" #' model <- read.ml(path) #' } +#' @note read.ml since 2.0.0 read.ml <- function(path) { path <- suppressWarnings(normalizePath(path)) jobj <- callJStatic("org.apache.spark.ml.r.RWrappers", "load", path) @@ -558,6 +579,7 @@ read.ml <- function(path) { #' df <- createDataFrame(ovarian) #' model <- spark.survreg(df, Surv(futime, fustat) ~ ecog_ps + rx) #' } +#' @note spark.survreg since 2.0.0 setMethod("spark.survreg", signature(data = "SparkDataFrame", formula = "formula"), function(data, formula, ...) { formula <- paste(deparse(formula), collapse = "") @@ -581,6 +603,7 @@ setMethod("spark.survreg", signature(data = "SparkDataFrame", formula = "formula #' model <- spark.survreg(trainingData, Surv(futime, fustat) ~ ecog_ps + rx) #' summary(model) #' } +#' @note summary(AFTSurvivalRegressionModel) since 2.0.0 setMethod("summary", signature(object = "AFTSurvivalRegressionModel"), function(object, ...) { jobj <- object@jobj @@ -608,6 +631,7 @@ setMethod("summary", signature(object = "AFTSurvivalRegressionModel"), #' predicted <- predict(model, testData) #' showDF(predicted) #' } +#' @note predict(AFTSurvivalRegressionModel) since 2.0.0 setMethod("predict", signature(object = "AFTSurvivalRegressionModel"), function(object, newData) { return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf))) |