```
library(sjPlot)
tab_xtab(df$VAR1,df$VAR2,
show.cell.prc = FALSE,
show.row.prc = TRUE,
show.col.prc = FALSE,
show.legend = TRUE,
show.na = FALSE,
show.summary=TRUE)
```

# Work with categorical variables

# Frequencies

When you want to check whether, for instance, more male are present in a specific group, you can get a clean contingency table with a chi-square test by following code. Of course, you can work with all the different options as you wish:

Group | Gender | Total | |
---|---|---|---|

Male | Female | ||

Group 1 | 28 50 % |
28 50 % |
56 100 % |

Group 2 | 22 45.8 % |
26 54.2 % |
48 100 % |

Total | 50 48.1 % |
54 51.9 % |
104 100 % |

χ^{2}=0.052 · df=1 · φ=0.042 · p=0.820 |

observed values

% within Group

# (M)ANOVA

The `manovaR`

function of the `CaviR`

package allows to have a full and informative overview of the role of a categorical variable in prediction of several numeric variables.

The function provides:

descriptive statistics for each level of the categorical variable

univariate analyses with a

*p*-value for statistical significance and a partial eta-squared for practical significancemultivariate analysis with the Wilks’ Lambda

small when

*η*> .0099_{p}^{2}medium when

*η*> .0588_{p}^{2}large when

*η*>.1379_{p}^{2}

```
library(CaviR)
manovaR(data[,c('Group','Autonomy','Vitality','Persistence')],
stand=TRUE, sign = 0.05, tukey = TRUE)
```

`[1] "Grouping variable has only 2 levels. Tukey not applicable"`

variables | Group 1 | Group 2 | F-value | p-value |
| eta-squared |
---|---|---|---|---|---|---|

Autonomy | 3.37 (±0.92) | 2.31 (±1.09) | 57.48 | <.001 | *** | 0.37 |

Vitality | 3.37 (±0.92) | 2.31 (±1.09) | 41.78 | <.001 | *** | 0.29 |

Persistence | 3.37 (±0.92) | 2.31 (±1.09) | 28.18 | <.001 | *** | 0.22 |

Wilks Lambda = 0.607,F(3,95) = 20.479 , p = <.001 |

When the categorical predictor has more than two levels, the function adds the solution of a multicomparison tukey post-hoc analyses to the table in letters. These letters are sorted based on the descriptives.

```
library(CaviR)
manovaR(data[,c('Groups','Autonomy','Vitality','Persistence')],
stand=TRUE, sign = 0.05, tukey = TRUE)
```

variables | Group 1 | Group 2 | Group 3 | Group 4 | F-value | p-value |
| eta-squared |
---|---|---|---|---|---|---|---|---|

Autonomy | 4.31 (±1.19) B | 4.23 (±0.95) B | 2.73 (±1.46) A | 2.16 (±1.16) A | 20.18 | <.001 | *** | 0.38 |

Vitality | 4.23 (±1.02) B | 4.27 (±0.87) B | 2.69 (±1.40) A | 2.83 (±1.40) A | 13.72 | <.001 | *** | 0.30 |

Persistence | 3.36 (±1.08) BC | 3.38 (±0.78) C | 2.64 (±1.15) AB | 1.96 (±0.93) A | 11.67 | <.001 | *** | 0.26 |

Wilks Lambda = 0.534,F(9,226.488) = 7.396 , p = <.001 |

## References

*Statistical Power Analysis for the Behavioral Sciences*. Academic Press.