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Pairwise contingency table analyses

Source: R/pairwise-contingency-table.R
pairwise_contingency_table.Rd

Pairwise Fisher's exact tests as post hoc tests for contingency table analyses, with effect sizes (Cramer's V) and p-value adjustment for multiple comparisons.

Usage

pairwise_contingency_table(
  data,
  x,
  y,
  counts = NULL,
  p.adjust.method = "holm",
  digits = 2L,
  conf.level = 0.95,
  alternative = "two.sided",
  ...
)

Arguments

data

A data frame (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not be accepted. Additionally, grouped data frames from {dplyr} should be ungrouped before they are entered as data.

x

The variable to use as the rows in the contingency table.

y

The variable to use as the columns in the contingency table. Default is NULL. If NULL, one-sample proportion test (a goodness of fit test) will be run for the x variable.

counts

The variable in data containing counts, or NULL if each row represents a single observation.

p.adjust.method

Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".

digits

Number of digits for rounding or significant figures. May also be "signif" to return significant figures or "scientific" to return scientific notation. Control the number of digits by adding the value as suffix, e.g. digits = "scientific4" to have scientific notation with 4 decimal places, or digits = "signif5" for 5 significant figures (see also signif()).

conf.level

Scalar between 0 and 1 (default: 95% confidence/credible intervals, 0.95). If NULL, no confidence intervals will be computed.

alternative

A character string specifying the alternative hypothesis; Controls the type of CI returned: "two.sided" (default, two-sided CI), "greater" or "less" (one-sided CI). Partial matching is allowed (e.g., "g", "l", "two"...). See section One-Sided CIs in the effectsize_CIs vignette.

...

Additional arguments (currently ignored).

Value

The returned tibble data frame can contain some or all of the following columns (the exact columns will depend on the statistical test):

  • statistic: the numeric value of a statistic

  • df: the numeric value of a parameter being modeled (often degrees of freedom for the test)

  • df.error and df: relevant only if the statistic in question has two degrees of freedom (e.g. anova)

  • p.value: the two-sided p-value associated with the observed statistic

  • method: the name of the inferential statistical test

  • estimate: estimated value of the effect size

  • conf.low: lower bound for the effect size estimate

  • conf.high: upper bound for the effect size estimate

  • conf.level: width of the confidence interval

  • conf.method: method used to compute confidence interval

  • conf.distribution: statistical distribution for the effect

  • effectsize: the name of the effect size

  • n.obs: number of observations

  • expression: pre-formatted expression containing statistical details

For examples, see data frame output vignette.

Pairwise contingency table tests

The table below provides summary about:

  • statistical test carried out for inferential statistics

  • type of effect size estimate and a measure of uncertainty for this estimate

  • functions used internally to compute these details

Hypothesis testing

Testp-value adjustment?Function used
Fisher's exact testYesstats::fisher.test()

Effect size estimation

Effect sizeCI available?Function used
Cramer's VYeseffectsize::cramers_v()

Citation

Patil, I., (2021). statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details. Journal of Open Source Software, 6(61), 3236, https://doi.org/10.21105/joss.03236

Examples

# for reproducibility
set.seed(123)
library(statsExpressions)

# pairwise Fisher's exact tests with Holm adjustment
pairwise_contingency_table(
  data = mtcars,
  x = cyl,
  y = am,
  p.adjust.method = "holm"
)
#> # A tibble: 3 × 14
#>   group1 group2 p.value p.value.adj estimate conf.level conf.low conf.high
#>   <chr>  <chr>    <dbl>       <dbl>    <dbl>      <dbl>    <dbl>     <dbl>
#> 1 4      6      0.332        0.560     0.180       0.95        0     0.743
#> 2 4      8      0.00514      0.0154    0.568       0.95        0     0.983
#> 3 6      8      0.280        0.560     0.229       0.95        0     0.728
#>   effectsize        conf.method conf.distribution p.adjust.method
#>   <chr>             <chr>       <chr>             <chr>          
#> 1 Cramer's V (adj.) ncp         chisq             Holm           
#> 2 Cramer's V (adj.) ncp         chisq             Holm           
#> 3 Cramer's V (adj.) ncp         chisq             Holm           
#>   test                expression
#>   <chr>               <list>    
#> 1 Fisher's exact test <language>
#> 2 Fisher's exact test <language>
#> 3 Fisher's exact test <language>

# with counts data and Bonferroni adjustment
pairwise_contingency_table(
  data = as.data.frame(Titanic),
  x = Class,
  y = Survived,
  counts = Freq,
  p.adjust.method = "bonferroni"
)
#> # A tibble: 6 × 14
#>   group1 group2  p.value p.value.adj estimate conf.level conf.low conf.high
#>   <chr>  <chr>     <dbl>       <dbl>    <dbl>      <dbl>    <dbl>     <dbl>
#> 1 1st    2nd    2.78e- 7    1.67e- 6    0.207       0.95   0.125     0.287 
#> 2 1st    3rd    3.68e-30    2.21e-29    0.357       0.95   0.296     0.419 
#> 3 1st    Crew   1.81e-34    1.09e-33    0.359       0.95   0.302     0.415 
#> 4 2nd    3rd    8.19e- 7    4.91e- 6    0.157       0.95   0.0926    0.220 
#> 5 2nd    Crew   2.77e- 8    1.66e- 7    0.164       0.95   0.105     0.222 
#> 6 3rd    Crew   5.98e- 1    1   e+ 0    0           0.95   0         0.0581
#>   effectsize        conf.method conf.distribution p.adjust.method
#>   <chr>             <chr>       <chr>             <chr>          
#> 1 Cramer's V (adj.) ncp         chisq             Bonferroni     
#> 2 Cramer's V (adj.) ncp         chisq             Bonferroni     
#> 3 Cramer's V (adj.) ncp         chisq             Bonferroni     
#> 4 Cramer's V (adj.) ncp         chisq             Bonferroni     
#> 5 Cramer's V (adj.) ncp         chisq             Bonferroni     
#> 6 Cramer's V (adj.) ncp         chisq             Bonferroni     
#>   test                expression
#>   <chr>               <list>    
#> 1 Fisher's exact test <language>
#> 2 Fisher's exact test <language>
#> 3 Fisher's exact test <language>
#> 4 Fisher's exact test <language>
#> 5 Fisher's exact test <language>
#> 6 Fisher's exact test <language>

# no p-value adjustment
pairwise_contingency_table(
  data = mtcars,
  x = cyl,
  y = am,
  p.adjust.method = "none"
)
#> # A tibble: 3 × 14
#>   group1 group2 p.value p.value.adj estimate conf.level conf.low conf.high
#>   <chr>  <chr>    <dbl>       <dbl>    <dbl>      <dbl>    <dbl>     <dbl>
#> 1 4      6      0.332       0.332      0.180       0.95        0     0.743
#> 2 4      8      0.00514     0.00514    0.568       0.95        0     0.983
#> 3 6      8      0.280       0.280      0.229       0.95        0     0.728
#>   effectsize        conf.method conf.distribution p.adjust.method
#>   <chr>             <chr>       <chr>             <chr>          
#> 1 Cramer's V (adj.) ncp         chisq             None           
#> 2 Cramer's V (adj.) ncp         chisq             None           
#> 3 Cramer's V (adj.) ncp         chisq             None           
#>   test                expression
#>   <chr>               <list>    
#> 1 Fisher's exact test <language>
#> 2 Fisher's exact test <language>
#> 3 Fisher's exact test <language>