test_between_factors {cola} | R Documentation

## Test whether a list of factors are correlated

### Description

Test whether a list of factors are correlated

### Usage

    test_between_factors(x, y = NULL, all_factors = FALSE, verbose = FALSE)

### Arguments

`x` | A data frame or a vector which contains discrete or continuous variables. if `y` is omit, pairwise testing for all columns in `x` is performed.
`y` | A data frame or a vector which contains discrete or continuous variables.
`all_factors` | Are all columns in `x` and `y` enforced to be factors?
`verbose` | Whether to print messages.

### Details

Pairwise test is applied to every two columns in the data frames. Methods are:

- two numeric variables: correlation test by `cor.test` is applied (Spearman method);

- two character or factor variables: `chisq.test` is applied;

- one numeric variable and one character/factor variable: oneway ANOVA test by `oneway.test` is applied.

This function can be used to test the correlation between the predicted classes and other known factors.

### Value

A matrix of p-values. If there are NA values, basically it means there are no efficient data points to perform the test.

### Author(s)

Zuguang Gu <z.gu@dkfz.de>

### Examples

    df = data.frame(
        v1 = rnorm(100),
        v2 = sample(letters[1:3], 100, replace = TRUE),
        v3 = sample(LETTERS[5:6], 100, replace = TRUE)
    )
    test_between_factors(df)
    x = runif(100)
    test_between_factors(x, df)

[Package cola version 1.6.0 Index]