chisqTest: Chi-Square Test

In Rita: Automated Transformations, Normality Testing, and Reporting

Chi-Square Test

Description

This function computes the chi-square test for normality.

Usage

chisqTest(data, alpha = 0.05, j = 1, df = 3)

Arguments

data	Data of a univariate distribution for which the test statistic is computed (vector)
alpha	The two-sided decision threshold used for hypothesis-testing
j	The # hypotheses tested; used to compute a Bonferonni correction, if applicable; should remain at its default if multiple testing is not an issue (scalar)
df	The degrees of freedom used to test for significance against the sampling distribution (scalar)

Details

Bins are created by cutting the data to ensure that values within these intervals would be equally probable if data are normal (Moore, 1986). By default, this function assumes that all relevant parameters (mu, sigma) are estimators, fixing the degrees of freedom at df = 3.

Value

An object including the test statistic, p-value, and a significance flag (list)

References

Moore, D.S., (1986) Tests of the chi-squared type. In: D'agostino, R.B. and Stephens, M.A., eds.: Goodness-of-Fit Techniques. Marcel Dekker, New York.

Examples

values <- rnorm(100)
x <- chisqTest(data = values)

Rita documentation built on March 18, 2022, 6:36 p.m.