`chisq_test.Rd`

Performs chi-squared tests, including goodness-of-fit, homogeneity and independence tests.

chisq_test(x, y = NULL, correct = TRUE, p = rep(1/length(x), length(x)), rescale.p = FALSE, simulate.p.value = FALSE, B = 2000) pairwise_chisq_gof_test(x, p.adjust.method = "holm", ...) pairwise_chisq_test_against_p(x, p = rep(1/length(x), length(x)), p.adjust.method = "holm", ...) chisq_descriptives(res.chisq) expected_freq(res.chisq) observed_freq(res.chisq) pearson_residuals(res.chisq) std_residuals(res.chisq)

x | a numeric vector or matrix. |
---|---|

y | a numeric vector; ignored if |

correct | a logical indicating whether to apply continuity
correction when computing the test statistic for 2 by 2 tables: one
half is subtracted from all \(|O - E|\) differences; however, the
correction will not be bigger than the differences themselves. No correction
is done if |

p | a vector of probabilities of the same length of |

rescale.p | a logical scalar; if TRUE then |

simulate.p.value | a logical indicating whether to compute p-values by Monte Carlo simulation. |

B | an integer specifying the number of replicates used in the Monte Carlo test. |

p.adjust.method | method to adjust p values for multiple comparisons. Used when pairwise comparisons are performed. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none". |

... | other arguments passed to the function |

res.chisq | an object of class |

return a data frame with some the following columns:

`n`

: the number of participants.`group, group1, group2`

: the categories or groups being compared.`statistic`

: the value of Pearson's chi-squared test statistic.`df`

: the degrees of freedom of the approximate chi-squared distribution of the test statistic. NA if the p-value is computed by Monte Carlo simulation.`p`

: p-value.`p.adj`

: the adjusted p-value.`method`

: the used statistical test.`p.signif, p.adj.signif`

: the significance level of p-values and adjusted p-values, respectively.`observed`

: observed counts.`expected`

: the expected counts under the null hypothesis.

The **returned object has an attribute called args**, which is a list
holding the test arguments.

`chisq_test`

: performs chi-square tests including goodness-of-fit, homogeneity and independence tests.`pairwise_chisq_gof_test`

: perform pairwise comparisons between groups following a global chi-square goodness of fit test.`pairwise_chisq_test_against_p`

: perform pairwise comparisons after a global chi-squared test for given probabilities. For each group, the observed and the expected proportions are shown. Each group is compared to the sum of all others.`chisq_descriptives`

: returns the descriptive statistics of the chi-square test. These include, observed and expected frequencies, proportions, residuals and standardized residuals.`expected_freq`

: returns the expected counts from the chi-square test result.`observed_freq`

: returns the observed counts from the chi-square test result.`pearson_residuals`

: returns the Pearson residuals,`(observed - expected) / sqrt(expected)`

.`std_residuals`

: returns the standardized residuals

# Chi-square goodness of fit test #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% tulip <- c(red = 81, yellow = 50, white = 27) # Q1: Are the colors equally common? chisq_test(tulip)#> # A tibble: 1 x 6 #> n statistic p df method p.signif #> * <int> <dbl> <dbl> <dbl> <chr> <chr> #> 1 3 27.9 0.00000088 2 Chi-square test ****pairwise_chisq_gof_test(tulip)#> # A tibble: 3 x 8 #> n group1 group2 statistic p df p.adj p.adj.signif #> * <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 2 red yellow 7.34 0.00676 1 0.0135 * #> 2 2 red white 27 0.000000203 1 0.000000609 **** #> 3 2 yellow white 6.87 0.00876 1 0.0135 *#> # A tibble: 1 x 6 #> n statistic p df method p.signif #> * <int> <dbl> <dbl> <dbl> <chr> <chr> #> 1 3 0.203 0.904 2 Chi-square test ns#> # A tibble: 3 x 9 #> group observed expected n statistic p df p.adj p.adj.signif #> * <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 red 81 79 2 0.101 0.75 1 1 ns #> 2 yellow 50 52.7 2 0.203 0.653 1 1 ns #> 3 white 27 26.3 2 0.0203 0.887 1 1 ns# Homogeneity of proportions between groups #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Data: Titanic xtab <- as.table(rbind( c(203, 118, 178, 212), c(122, 167, 528, 673) )) dimnames(xtab) <- list( Survived = c("Yes", "No"), Class = c("1st", "2nd", "3rd", "Crew") ) xtab#> Class #> Survived 1st 2nd 3rd Crew #> Yes 203 118 178 212 #> No 122 167 528 673# Chi-square test chisq_test(xtab)#> # A tibble: 1 x 6 #> n statistic p df method p.signif #> * <dbl> <dbl> <dbl> <int> <chr> <chr> #> 1 2201 190. 5.00e-41 3 Chi-square test ****#> # A tibble: 6 x 5 #> group1 group2 p p.adj p.adj.signif #> * <chr> <chr> <dbl> <dbl> <chr> #> 1 1st 2nd 3.13e- 7 9.38e- 7 **** #> 2 1st 3rd 2.55e-30 1.27e-29 **** #> 3 1st Crew 1.62e-35 9.73e-35 **** #> 4 2nd 3rd 6.90e- 7 1.38e- 6 **** #> 5 2nd Crew 1.94e- 8 7.75e- 8 **** #> 6 3rd Crew 6.03e- 1 6.03e- 1 ns