f_chisq_test: Chi-squared Test with Post-hoc Analysis

Description

Performs a chi-squared test chisq.test, then automatically conducts post-hoc analysis if the test is significant. The function provides adjusted p-values for each cell in the contingency table using a specified correction method.

Usage

f_chisq_test(
  x,
  y,
  p = NULL,
  method = "bonferroni",
  digits = 3,
  alpha = 0.05,
  force_posthoc = FALSE,
  ...
)

Value

An object of class f_chisq_test containing:

chisq_test_output: The output from chisq.test.
adjusted_p_values: Matrix of adjusted p-values (for table/matrix input).
observed_vs_adj_p_value: Interleaved table of observed values and adjusted p-values.
stdres_vs_adj_p_value: Interleaved table of standardized residuals and adjusted p-values.
adj_p_values: Vector of adjusted p-values (for vector input).
posthoc_output_table: Data frame with observed values, expected values, standardized residuals, and adjusted p-values (for vector input).

Arguments

x: A numeric vector (or factor), or a contingency table in matrix or table form. If a data frame is entered the function will try to convert it to a table.
y: A numeric vector; ignored if x is a matrix, table or data.frame. If x is a factor, y should be a factor of the same length.
p: A vector of probabilities of the same length as x. Default is NULL. An error is given if any entry of p is negative.
method: Character string specifying the adjustment method for p-values. Default is "bonferroni". Other options include "holm", "hochberg", "hommel", "BH", "BY", "fdr", and "none".
digits: Integer specifying the number of decimal places for rounding. Default is 3.
alpha: Numeric threshold for significance. Default is 0.05.
force_posthoc: Logical indicating whether to perform post-hoc tests even if the chi-squared test is not significant. Default is FALSE.
...: Additional arguments passed to chisq.test.

Author

Sander H. van Delden plantmind@proton.me

Details

The function first performs a chi-squared test using chisq.test. If the test is significant (p < alpha) or if force_posthoc = TRUE, it conducts post-hoc analysis by examining the standardized residuals. The p-values for these residuals are adjusted using the specified method to control for multiple comparisons.

If the input is a data frame, the function attempts to convert it to a table and displays the resulting table for verification.

References

This function implements a post-hoc analysis for chi-squared tests inspired by the methodology in:

Beasley, T. M., & Schumacker, R. E. (1995). Multiple Regression Approach to Analyzing Contingency Tables: Post Hoc and Planned Comparison Procedures. The Journal of Experimental Education, 64(1), 79-93.

The implementation draws inspiration from the 'chisq.posthoc.test' package by Daniel Ebbert.

Examples

Run this code

# Chi.square on independence: Association between two variables.
# Create a contingency table.
my_table <- as.table(rbind(c(100, 150, 50), c(120, 90, 40)))
dimnames(my_table) <- list(Gender = c("Male", "Female"),
                           Response = c("Agree", "Neutral", "Disagree"))

# Perform chi-squared test with post-hoc analysis.
f_chisq_test(my_table)

# Use a different adjustment method.
f_chisq_test(my_table, method = "holm")

# Other forms still work like Goodness-of-Fit: Match to theoretical distribution.
# Observed frequencies of rolling with a die 1 - 6.
observed <- c(2, 2, 10, 20, 15, 11)

# Expected probabilities under a fair die.
expected_probs <- rep(1/6, 6)

# Chi-Square Goodness-of-Fit Test.
f_chisq_test(x = observed, p = expected_probs)

Run the code above in your browser using DataLab