## Introduction

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.

M <- as.table(rbind(c(762, 327, 468), c(484, 239, 477)))
dimnames(M) <- list(gender = c("F", "M"),
party = c("Democrat","Independent", "Republican"))
chisq.test(M)
#>
#>  Pearson's Chi-squared test
#>
#> data:  M
#> X-squared = 30.07, df = 2, p-value = 2.954e-07

## Standarized residuals

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

## Post Hoc Analysis

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*