address review comments

Author: wangmiao1981

R/pkg/R/mllib.R

@@ -748,10 +748,10 @@ setMethod("predict", signature(object = "KMeansModel"),
 #' sparkR.session()
 #' # binary logistic regression
 #' label <- c(0.0, 0.0, 0.0, 1.0, 1.0)
-#' feature <- c(1.1419053, 0.9194079, -0.9498666, -1.1069903, 0.2809776)
-#' binary_data <- as.data.frame(cbind(label, feature))
+#' features <- c(1.1419053, 0.9194079, -0.9498666, -1.1069903, 0.2809776)
+#' binary_data <- as.data.frame(cbind(label, features))
 #' binary_df <- createDataFrame(binary_data)
-#' blr_model <- spark.logit(binary_df, label ~ feature, thresholds = 1.0)
+#' blr_model <- spark.logit(binary_df, label ~ features, thresholds = 1.0)
 #' blr_predict <- collect(select(predict(blr_model, binary_df), "prediction"))
 #'
 #' # summary of binary logistic regression
@@ -769,11 +769,11 @@ setMethod("predict", signature(object = "KMeansModel"),
 #' # multinomial logistic regression
 #'
 #' label <- c(0.0, 1.0, 2.0, 0.0, 0.0)
-#' feature1 <- c(4.845940, 5.64480, 7.430381, 6.464263, 5.555667)
-#' feature2 <- c(2.941319, 2.614812, 2.162451, 3.339474, 2.970987)
-#' feature3 <- c(1.322733, 1.348044, 3.861237, 9.686976, 3.447130)
-#' feature4 <- c(1.3246388, 0.5510444, 0.9225810, 1.2147881, 1.6020842)
-#' data <- as.data.frame(cbind(label, feature1, feature2, feature3, feature4))
+#' features1 <- c(4.845940, 5.64480, 7.430381, 6.464263, 5.555667)
+#' features2 <- c(2.941319, 2.614812, 2.162451, 3.339474, 2.970987)
+#' features3 <- c(1.322733, 1.348044, 3.861237, 9.686976, 3.447130)
+#' features4 <- c(1.3246388, 0.5510444, 0.9225810, 1.2147881, 1.6020842)
+#' data <- as.data.frame(cbind(label, features1, features2, features3, features4))
 #' df <- createDataFrame(data)
 #'
 #' # Note that summary of multinomial logistic regression is not implemented yet

R/pkg/inst/tests/testthat/test_mllib.R

@@ -662,15 +662,15 @@ test_that("spark.logit", {
   blr_summary <- summary(blr_model)
   blr_fmeasure <- collect(select(blr_summary$fMeasureByThreshold, "threshold", "F-Measure"))
   expect_equal(blr_fmeasure$threshold, c(0.6565513, 0.6214563, 0.3325291, 0.2115995, 0.1778653),
-  tolerance = 1e-4)
+               tolerance = 1e-4)
   expect_equal(blr_fmeasure$"F-Measure", c(0.6666667, 0.5000000, 0.8000000, 0.6666667, 0.5714286),
-  tolerance = 1e-4)
+               tolerance = 1e-4)
   blr_precision <- collect(select(blr_summary$precisionByThreshold, "threshold", "precision"))
   expect_equal(blr_precision$precision, c(1.0000000, 0.5000000, 0.6666667, 0.5000000, 0.4000000),
-  tolerance = 1e-4)
+               tolerance = 1e-4)
   blr_recall <- collect(select(blr_summary$recallByThreshold, "threshold", "recall"))
   expect_equal(blr_recall$recall, c(0.5000000, 0.5000000, 1.0000000, 1.0000000, 1.0000000),
-  tolerance = 1e-4)
+               tolerance = 1e-4)
 
   # test model save and read
   modelPath <- tempfile(pattern = "spark-logisticRegression", fileext = ".tmp")