[SPARK-20906][SPARKR] Add API doc example for Constrained Logistic Re…

…gression

## What changes were proposed in this pull request?

doc only changes

## How was this patch tested?

manual

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #20380 from felixcheung/rclrdoc.

(cherry picked from commit e18d6f5)
Signed-off-by: Felix Cheung <felixcheung@apache.org>

R/pkg/R/mllib_classification.R

@@ -279,11 +279,24 @@ function(object, path, overwrite = FALSE) {
 #' savedModel <- read.ml(path)
 #' summary(savedModel)
 #'
-#' # multinomial logistic regression
+#' # binary logistic regression against two classes with
+#' # upperBoundsOnCoefficients and upperBoundsOnIntercepts
+#' ubc <- matrix(c(1.0, 0.0, 1.0, 0.0), nrow = 1, ncol = 4)
+#' model <- spark.logit(training, Species ~ .,
+#'                       upperBoundsOnCoefficients = ubc,
+#'                       upperBoundsOnIntercepts = 1.0)
 #'
+#' # multinomial logistic regression
 #' model <- spark.logit(training, Class ~ ., regParam = 0.5)
 #' summary <- summary(model)
 #'
+#' # multinomial logistic regression with
+#' # lowerBoundsOnCoefficients and lowerBoundsOnIntercepts
+#' lbc <- matrix(c(0.0, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0, -1.0), nrow = 2, ncol = 4)
+#' lbi <- as.array(c(0.0, 0.0))
+#' model <- spark.logit(training, Species ~ ., family = "multinomial",
+#'                      lowerBoundsOnCoefficients = lbc,
+#'                      lowerBoundsOnIntercepts = lbi)
 #' }
 #' @note spark.logit since 2.1.0
 setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula"),

R/pkg/tests/fulltests/test_mllib_classification.R

@@ -124,7 +124,7 @@ test_that("spark.logit", {
   # Petal.Width   0.42122607
   # nolint end
 
-  # Test multinomial logistic regression againt three classes
+  # Test multinomial logistic regression against three classes
   df <- suppressWarnings(createDataFrame(iris))
   model <- spark.logit(df, Species ~ ., regParam = 0.5)
   summary <- summary(model)
@@ -196,7 +196,7 @@ test_that("spark.logit", {
   #
   # nolint end
 
-  # Test multinomial logistic regression againt two classes
+  # Test multinomial logistic regression against two classes
   df <- suppressWarnings(createDataFrame(iris))
   training <- df[df$Species %in% c("versicolor", "virginica"), ]
   model <- spark.logit(training, Species ~ ., regParam = 0.5, family = "multinomial")
@@ -208,7 +208,7 @@ test_that("spark.logit", {
   expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1))
   expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1))
 
-  # Test binomial logistic regression againt two classes
+  # Test binomial logistic regression against two classes
   model <- spark.logit(training, Species ~ ., regParam = 0.5)
   summary <- summary(model)
   coefsR <- c(-6.08, 0.25, 0.16, 0.48, 1.04)
@@ -239,7 +239,7 @@ test_that("spark.logit", {
   prediction2 <- collect(select(predict(model2, df2), "prediction"))
   expect_equal(sort(prediction2$prediction), c("0.0", "0.0", "0.0", "0.0", "0.0"))
 
-  # Test binomial logistic regression againt two classes with upperBoundsOnCoefficients
+  # Test binomial logistic regression against two classes with upperBoundsOnCoefficients
   # and upperBoundsOnIntercepts
   u <- matrix(c(1.0, 0.0, 1.0, 0.0), nrow = 1, ncol = 4)
   model <- spark.logit(training, Species ~ ., upperBoundsOnCoefficients = u,
@@ -252,7 +252,7 @@ test_that("spark.logit", {
   expect_error(spark.logit(training, Species ~ ., upperBoundsOnCoefficients = as.array(c(1, 2)),
                            upperBoundsOnIntercepts = 1.0))
 
-  # Test binomial logistic regression againt two classes with lowerBoundsOnCoefficients
+  # Test binomial logistic regression against two classes with lowerBoundsOnCoefficients
   # and lowerBoundsOnIntercepts
   l <- matrix(c(0.0, -1.0, 0.0, -1.0), nrow = 1, ncol = 4)
   model <- spark.logit(training, Species ~ ., lowerBoundsOnCoefficients = l,