pairwiseComparisons (version 0.2.5)

pairwise_comparisons: Multiple pairwise comparison tests with tidy data

Description

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

Usage

pairwise_comparisons(
  data,
  x,
  y,
  type = "parametric",
  tr = 0.1,
  paired = FALSE,
  var.equal = FALSE,
  p.adjust.method = "holm",
  k = 2,
  ...
)
pairwise_p(
  data,
  x,
  y,
  type = "parametric",
  tr = 0.1,
  paired = FALSE,
  var.equal = FALSE,
  p.adjust.method = "holm",
  k = 2,
  ...
)

Arguments

data

A dataframe (or a tibble) 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.

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.

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.

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 = 2).

...

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.

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

• ns : > 0.05

• * : < 0.05

• ** : < 0.01

• *** : < 0.001

Examples

# NOT RUN {
# }
# NOT RUN {
# show all columns in a tibble
options(tibble.width = Inf)

#------------------- between-subjects design ----------------------------

# for reproducibility
set.seed(123)

library(pairwiseComparisons)

# parametric
# if `var.equal = TRUE`, then Student's *t*-test will be run
pairwise_comparisons(
  data = ggplot2::msleep,
  x = vore,
  y = brainwt,
  type = "parametric",
  var.equal = TRUE,
  paired = FALSE,
  p.adjust.method = "bonferroni"
)

# if `var.equal = FALSE`, then Games-Howell test will be run
pairwise_comparisons(
  data = ggplot2::msleep,
  x = vore,
  y = brainwt,
  type = "parametric",
  var.equal = FALSE,
  paired = FALSE,
  p.adjust.method = "bonferroni"
)

# non-parametric
pairwise_comparisons(
  data = ggplot2::msleep,
  x = vore,
  y = brainwt,
  type = "nonparametric",
  paired = FALSE,
  p.adjust.method = "none"
)

# robust
pairwise_comparisons(
  data = ggplot2::msleep,
  x = vore,
  y = brainwt,
  type = "robust",
  paired = FALSE,
  p.adjust.method = "fdr"
)

#------------------- within-subjects design ----------------------------

# for reproducibility
set.seed(123)

# parametric
pairwise_comparisons(
  data = bugs_long,
  x = condition,
  y = desire,
  type = "parametric",
  paired = TRUE,
  p.adjust.method = "BH"
)

# non-parametric
pairwise_comparisons(
  data = bugs_long,
  x = condition,
  y = desire,
  type = "nonparametric",
  paired = TRUE,
  p.adjust.method = "BY"
)

# robust
pairwise_comparisons(
  data = bugs_long,
  x = condition,
  y = desire,
  type = "robust",
  paired = TRUE,
  p.adjust.method = "hommel"
)
# }

Run the code above in your browser using DataCamp Workspace