## 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 4.502054 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
```