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Diffstat (limited to 'R/pkg/R/mllib.R')
-rw-r--r-- | R/pkg/R/mllib.R | 105 |
1 files changed, 105 insertions, 0 deletions
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R index 9a53f757b4..f321fd19b3 100644 --- a/R/pkg/R/mllib.R +++ b/R/pkg/R/mllib.R @@ -88,6 +88,13 @@ setClass("GaussianMixtureModel", representation(jobj = "jobj")) #' @note ALSModel since 2.1.0 setClass("ALSModel", representation(jobj = "jobj")) +#' S4 class that represents an KSTest +#' +#' @param jobj a Java object reference to the backing Scala KSTestWrapper +#' @export +#' @note KSTest since 2.1.0 +setClass("KSTest", representation(jobj = "jobj")) + #' Saves the MLlib model to the input path #' #' Saves the MLlib model to the input path. For more information, see the specific @@ -1310,3 +1317,101 @@ setMethod("write.ml", signature(object = "ALSModel", path = "character"), function(object, path, overwrite = FALSE) { write_internal(object, path, overwrite) }) + +#' (One-Sample) Kolmogorov-Smirnov Test +#' +#' @description +#' \code{spark.kstest} Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a +#' continuous distribution. +#' +#' By comparing the largest difference between the empirical cumulative +#' distribution of the sample data and the theoretical distribution we can provide a test for the +#' the null hypothesis that the sample data comes from that theoretical distribution. +#' +#' Users can call \code{summary} to obtain a summary of the test, and \code{print.summary.KSTest} +#' to print out a summary result. +#' +#' @param data a SparkDataFrame of user data. +#' @param testCol column name where the test data is from. It should be a column of double type. +#' @param nullHypothesis name of the theoretical distribution tested against. Currently only +#' \code{"norm"} for normal distribution is supported. +#' @param distParams parameters(s) of the distribution. For \code{nullHypothesis = "norm"}, +#' we can provide as a vector the mean and standard deviation of +#' the distribution. If none is provided, then standard normal will be used. +#' If only one is provided, then the standard deviation will be set to be one. +#' @param ... additional argument(s) passed to the method. +#' @return \code{spark.kstest} returns a test result object. +#' @rdname spark.kstest +#' @aliases spark.kstest,SparkDataFrame-method +#' @name spark.kstest +#' @seealso \href{http://spark.apache.org/docs/latest/mllib-statistics.html#hypothesis-testing}{ +#' MLlib: Hypothesis Testing} +#' @export +#' @examples +#' \dontrun{ +#' data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25)) +#' df <- createDataFrame(data) +#' test <- spark.ktest(df, "test", "norm", c(0, 1)) +#' +#' # get a summary of the test result +#' testSummary <- summary(test) +#' testSummary +#' +#' # print out the summary in an organized way +#' print.summary.KSTest(test) +#' } +#' @note spark.kstest since 2.1.0 +setMethod("spark.kstest", signature(data = "SparkDataFrame"), + function(data, testCol = "test", nullHypothesis = c("norm"), distParams = c(0, 1)) { + tryCatch(match.arg(nullHypothesis), + error = function(e) { + msg <- paste("Distribution", nullHypothesis, "is not supported.") + stop(msg) + }) + if (nullHypothesis == "norm") { + distParams <- as.numeric(distParams) + mu <- ifelse(length(distParams) < 1, 0, distParams[1]) + sigma <- ifelse(length(distParams) < 2, 1, distParams[2]) + jobj <- callJStatic("org.apache.spark.ml.r.KSTestWrapper", + "test", data@sdf, testCol, nullHypothesis, + as.array(c(mu, sigma))) + new("KSTest", jobj = jobj) + } +}) + +# Get the summary of Kolmogorov-Smirnov (KS) Test. +#' @param object test result object of KSTest by \code{spark.kstest}. +#' @return \code{summary} returns a list containing the p-value, test statistic computed for the +#' test, the null hypothesis with its parameters tested against +#' and degrees of freedom of the test. +#' @rdname spark.kstest +#' @aliases summary,KSTest-method +#' @export +#' @note summary(KSTest) since 2.1.0 +setMethod("summary", signature(object = "KSTest"), + function(object) { + jobj <- object@jobj + pValue <- callJMethod(jobj, "pValue") + statistic <- callJMethod(jobj, "statistic") + nullHypothesis <- callJMethod(jobj, "nullHypothesis") + distName <- callJMethod(jobj, "distName") + distParams <- unlist(callJMethod(jobj, "distParams")) + degreesOfFreedom <- callJMethod(jobj, "degreesOfFreedom") + + list(p.value = pValue, statistic = statistic, nullHypothesis = nullHypothesis, + nullHypothesis.name = distName, nullHypothesis.parameters = distParams, + degreesOfFreedom = degreesOfFreedom) + }) + +# Prints the summary of KSTest + +#' @rdname spark.kstest +#' @param x test result object of KSTest by \code{spark.kstest}. +#' @export +#' @note print.summary.KSTest since 2.1.0 +print.summary.KSTest <- function(x, ...) { + jobj <- x@jobj + summaryStr <- callJMethod(jobj, "summary") + cat(summaryStr) + invisible(summaryStr) +} |