When computing Pearson’s Chi-squared Test for Count Data the only result you get is that you know that there is a significant difference in the data and not which parts of the data are responsible for this. Here you see the example from the chisq.test documentation.
As a form of post hoc analysis the standarized residuals can be analysed. A rule of thumb is that standarized residuals of above two show significance.
chisq.results <- chisq.test(M) chisq.results$stdres #> party #> gender Democrat Independent Republican #> F 4.5020535 0.6994517 -5.3159455 #> M -4.5020535 -0.6994517 5.3159455
However, the above two rule is a rule of thumb. These standarized residuals can be used to calculate p-values, which is what this package is designed for as shown in the following example.
chisq.posthoc.test(M, method = "bonferroni") #> 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*