Simplified Chi Square — chi.sq • vannstats

This function simplifies the call for Pearson's Chi Square test (chi.sq) on a given data frame.

Usage

chi.sq(
  df,
  var1,
  var2,
  correct = FALSE,
  post = FALSE,
  plot = FALSE,
  cramer = FALSE
)

Arguments

df: data frame to read in.
var1: the dependent/outcome variable, \(Y\).
var2: the main independent/predictor variable, \(X\).
correct: logical (default set to F). When set to correct = T, will employ Yates' continuity correction (for data that violate the normality assumption).
post: logical (default set to F). When set to post = T, will return results of post-hoc (Z) tests of the standardized residual for each cell (the standardized difference between observed and expected frequencies), using Bonferroni's alpha adjustment, and returns an adjusted p-value for each cell/comparison.
plot: logical (default set to F). When set to plot = T, will print a corrplot-style plot for showing both the value of difference between the standardized residual (Z) and the related level of significance of this difference (for each cell comparison) as well as a gradient color representing the relative value of this residual. Will also return results of post-hoc (Z) tests of the standardized residual for each cell (the standardized difference between observed and expected frequencies), using Bonferroni's alpha adjustment, and returns an adjusted p-value for each cell/comparison.
cramer: logical (default set to F). When set to post = T, will return results of Cramer's V, a measure of the strength of the association between the two variables.

Value

This function returns the summary results table for a Pearson's Chi Square test, examining the relationship between var1 from data frame df, and var2.

Examples

data <- mtcars

x2 <- chi.sq(data,vs,am)
summary(x2)
#> Call:
#> chi.sq(df = data, var1 = vs, var2 = am)
#> 
#> Pearson's Chi-squared test: 
#> 
#>       χ² Critical χ² df p-value
#>  0.90688     3.84100  1  0.3409
#>