ml_chisquare_test: Chi-square hypothesis testing for categorical data.

In sparklyr: R Interface to Apache Spark

Chi-square hypothesis testing for categorical data.

Description

Conduct Pearson's independence test for every feature against the label. For each feature, the (feature, label) pairs are converted into a contingency matrix for which the Chi-squared statistic is computed. All label and feature values must be categorical.

Usage

ml_chisquare_test(x, features, label)

Arguments

x	A tbl_spark.
features	The name(s) of the feature columns. This can also be the name of a single vector column created using ft_vector_assembler().
label	The name of the label column.

Value

A data frame with one row for each (feature, label) pair with p-values, degrees of freedom, and test statistics.

Examples

## Not run: 
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

features <- c("Petal_Width", "Petal_Length", "Sepal_Length", "Sepal_Width")

ml_chisquare_test(iris_tbl, features = features, label = "Species")

## End(Not run)

sparklyr documentation built on May 29, 2024, 2:58 a.m.