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

chisq.posthoc.test(x, method = "bonferroni", round = 4, alpha = 0.05,
  ...)

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 4.

alpha

The alpha value to the compare the p values to. Defaults to 0.05.

...

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, alpha = 0.05)
#> Dimension Value Democrat Independent Republican #> 1 F Residuals 4.50205352108671 0.6994517 -5.31594554270493 #> 2 F p values 0* 1.0000000 0* #> 3 M Residuals -4.50205352108671 -0.6994517 5.31594554270493 #> 4 M p values 0* 1.0000000 0*