chisq.posthoc.test: Perform post hoc analysis based on residuals of Pearson's...

In chisq.posthoc.test: A Post Hoc Analysis for Pearson's Chi-Squared Test for Count Data

Description

Perform post hoc analysis based on residuals of Pearson's Chi-squared Test for Count Data.

Usage

chisq.posthoc.test(x, method = "bonferroni", round = 6, ...)

Arguments

x	A matrix passed on to the chisq.test function.
method	The p adjustment method to be used. This is passed on to the p.adjust function.
round	Number of digits to round the p.value to. Defaults to 6.
...	Additional arguments passed on to the chisq.test function.

Value

A table with the adjusted p value for each x y combination.

References

Agresti, A. (2007). An Introduction to Categorical Data Analysis, 2nd ed. New York: John Wiley & Sons. Page 38.

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.

Examples

# Data from Agresti(2007) p.39
M <- as.table(rbind(c(762, 327, 468), c(484, 239, 477)))
dimnames(M) <- list(gender = c("F", "M"),
                   party = c("Democrat","Independent", "Republican"))

# Pass data matrix to chisq.posthoc.test function
chisq.posthoc.test(M)

Example output

  Dimension     Value  Democrat Independent Republican
1         F Residuals  4.502054   0.6994517  -5.315946
2         F  p values  0.000040   1.0000000   0.000001
3         M Residuals -4.502054  -0.6994517   5.315946
4         M  p values  0.000040   1.0000000   0.000001

chisq.posthoc.test documentation built on Oct. 30, 2019, 11:41 a.m.