# pairwise_comparisons: Multiple pairwise comparison tests with tidy data In IndrajeetPatil/pairwiseComparisons: Multiple Pairwise Comparison Tests

## Description

Calculate parametric, non-parametric, robust, and Bayes Factor pairwise comparisons between group levels with corrections for multiple testing.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```pairwise_comparisons( data, x, y, type = "parametric", paired = FALSE, var.equal = FALSE, tr = 0.1, bf.prior = 0.707, p.adjust.method = "holm", k = 2L, ... ) ```

## Arguments

 `data` A dataframe from which variables specified are to be taken. A matrix or tables will not be accepted. `x` The grouping variable from the dataframe `data`. `y` The response (a.k.a. outcome or dependent) variable from the dataframe `data`. `type` Type of statistic expected (`"parametric"` or `"nonparametric"` or `"robust"` or `"bayes"`).Corresponding abbreviations are also accepted: `"p"` (for parametric), `"np"` (nonparametric), `"r"` (robust), or `"bf"`resp. `paired` Logical that decides whether the experimental design is repeated measures/within-subjects or between-subjects. The default is `FALSE`. `var.equal` a logical variable indicating whether to treat the two variances as being equal. If `TRUE` then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used. `tr` Trim level for the mean when carrying out `robust` tests. If you get error stating "Standard error cannot be computed because of Winsorized variance of 0 (e.g., due to ties). Try to decrease the trimming level.", try to play around with the value of `tr`, which is by default set to `0.1`. Lowering the value might help. `bf.prior` A number between `0.5` and `2` (default `0.707`), the prior width to use in calculating Bayes factors. `p.adjust.method` Adjustment method for p-values for multiple comparisons. Possible methods are: `"holm"` (default), `"hochberg"`, `"hommel"`, `"bonferroni"`, `"BH"`, `"BY"`, `"fdr"`, `"none"`. `k` Number of digits after decimal point (should be an integer) (Default: `k = 2L`). `...` Current ignored.

## Value

A tibble dataframe containing two columns corresponding to group levels being compared with each other (`group1` and `group2`) and `p.value` column corresponding to this comparison. The dataframe will also contain a `p.value.label` column containing a label for this p-value, in case this needs to be displayed in `geom_ggsignif`. In addition to these common columns across the different types of statistics, there will be additional columns specific to the `type` of test being run.

This function provides a unified syntax to carry out pairwise comparison tests and internally relies on other packages to carry out these tests. For more details about the included tests, see the documentation for the respective functions:

• parametric : `stats::pairwise.t.test()` (paired) and `PMCMRplus::gamesHowellTest()` (unpaired)

• non-parametric : `PMCMRplus::durbinAllPairsTest()` (paired) and `PMCMRplus::kwAllPairsDunnTest()` (unpaired)

• robust : `WRS2::rmmcp()` (paired) and `WRS2::lincon()` (unpaired)

• Bayes Factor : `BayesFactor::ttestBF()`

The `significance` column asterisks indicate significance levels of p-values in the American Psychological Association (APA) mandated format:

• `ns` : > 0.05

• `*` : < 0.05

• `**` : < 0.01

• `***` : < 0.001

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101``` ```# for reproducibility set.seed(123) library(pairwiseComparisons) # show me all columns and make the column titles bold options(tibble.width = Inf, pillar.bold = TRUE) #------------------- between-subjects design ---------------------------- # parametric # if `var.equal = TRUE`, then Student's t-test will be run pairwise_comparisons( data = mtcars, x = cyl, y = wt, type = "parametric", var.equal = TRUE, paired = FALSE, p.adjust.method = "none" ) # if `var.equal = FALSE`, then Games-Howell test will be run pairwise_comparisons( data = mtcars, x = cyl, y = wt, type = "parametric", var.equal = FALSE, paired = FALSE, p.adjust.method = "bonferroni" ) # non-parametric (Dunn test) pairwise_comparisons( data = mtcars, x = cyl, y = wt, type = "nonparametric", paired = FALSE, p.adjust.method = "none" ) # robust (Yuen's trimmed means t-test) pairwise_comparisons( data = mtcars, x = cyl, y = wt, type = "robust", paired = FALSE, p.adjust.method = "fdr" ) # Bayes Factor (Student's t-test) pairwise_comparisons( data = mtcars, x = cyl, y = wt, type = "bayes", paired = FALSE ) #------------------- within-subjects design ---------------------------- # parametric (Student's t-test) pairwise_comparisons( data = bugs_long, x = condition, y = desire, type = "parametric", paired = TRUE, p.adjust.method = "BH" ) # non-parametric (Durbin-Conover test) pairwise_comparisons( data = bugs_long, x = condition, y = desire, type = "nonparametric", paired = TRUE, p.adjust.method = "BY" ) # robust (Yuen's trimmed means t-test) pairwise_comparisons( data = bugs_long, x = condition, y = desire, type = "robust", paired = TRUE, p.adjust.method = "hommel" ) # Bayes Factor (Student's t-test) pairwise_comparisons( data = bugs_long, x = condition, y = desire, type = "bayes", paired = TRUE ) ```

IndrajeetPatil/pairwiseComparisons documentation built on Oct. 20, 2020, 2:23 a.m.