wilcox_test: Wilcoxon Tests

Description

Provides a pipe-friendly framework to performs one and two sample Wilcoxon tests. Read more: Wilcoxon in R.

Usage

wilcox_test(
  data,
  formula,
  comparisons = NULL,
  ref.group = NULL,
  p.adjust.method = "holm",
  paired = FALSE,
  exact = NULL,
  alternative = "two.sided",
  mu = 0,
  conf.level = 0.95,
  detailed = FALSE
)
pairwise_wilcox_test(
  data,
  formula,
  comparisons = NULL,
  ref.group = NULL,
  p.adjust.method = "holm",
  detailed = FALSE,
  ...
)

Value

return a data frame with some of the following columns:

.y.: the y variable used in the test.
group1,group2: the compared groups in the pairwise tests.
n,n1,n2: Sample counts.
statistic: Test statistic used to compute the p-value.
p: p-value.
p.adj: the adjusted p-value.
method: the statistical test used to compare groups.
p.signif, p.adj.signif: the significance level of p-values and adjusted p-values, respectively.
estimate: an estimate of the location parameter (Only present if argument detailed = TRUE). This corresponds to the pseudomedian (for one-sample case) or to the difference of the location parameter (for two-samples case).
- The pseudomedian of a distribution F is the median of the distribution of (u+v)/2, where u and v are independent, each with distribution F. If F is symmetric, then the pseudomedian and median coincide.
- Note that in the two-sample case the estimator for the difference in location parameters does not estimate the difference in medians (a common misconception) but rather the median of the difference between a sample from x and a sample from y.
conf.low, conf.high: a confidence interval for the location parameter. (Only present if argument conf.int = TRUE.)

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

Arguments

data

a data.frame containing the variables in the formula.

formula

a formula of the form x ~ group where x is a numeric variable giving the data values and group is a factor with one or multiple levels giving the corresponding groups. For example, formula = TP53 ~ cancer_group.

comparisons

A list of length-2 vectors specifying the groups of interest to be compared. For example to compare groups "A" vs "B" and "B" vs "C", the argument is as follow: comparisons = list(c("A", "B"), c("B", "C"))

ref.group

a character string specifying the reference group. If specified, for a given grouping variable, each of the group levels will be compared to the reference group (i.e. control group).

If ref.group = "all", pairwise two sample tests are performed for comparing each grouping variable levels against all (i.e. basemean).

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".

paired

a logical indicating whether you want a paired test.

exact

a logical indicating whether an exact p-value should be computed.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

mu

a number specifying an optional parameter used to form the null hypothesis.

conf.level

confidence level of the interval.

detailed

logical value. Default is FALSE. If TRUE, a detailed result is shown.

...

other arguments to be passed to the function wilcox.test.

Functions

wilcox_test(): Wilcoxon test
pairwise_wilcox_test(): performs pairwise two sample Wilcoxon test.

Details

- pairwise_wilcox_test() applies the standard two sample Wilcoxon test to all possible pairs of groups. This method calls the wilcox.test(), so extra arguments are accepted.

- If a list of comparisons is specified, the result of the pairwise tests is filtered to keep only the comparisons of interest.The p-value is adjusted after filtering.

- For a grouped data, if pairwise test is performed, then the p-values are adjusted for each group level independently.

- a nonparametric confidence interval and an estimator for the pseudomedian (one-sample case) or for the difference of the location parameters x-y is computed, where x and y are the compared samples or groups. The column estimate and the confidence intervals are displayed in the test result when the option detailed = TRUE is specified in the wilcox_test() and pairwise_wilcox_test() functions. Read more about the calculation of the estimate in the details section of the R base function wilcox.test() documentation by typing ?wilcox.test in the R console.

Examples

Run this code

# Load data
#:::::::::::::::::::::::::::::::::::::::
data("ToothGrowth")
df <- ToothGrowth

# One-sample test
#:::::::::::::::::::::::::::::::::::::::::
df %>% wilcox_test(len ~ 1, mu = 0)


# Two-samples unpaired test
#:::::::::::::::::::::::::::::::::::::::::
df %>% wilcox_test(len ~ supp)

# Two-samples paired test
#:::::::::::::::::::::::::::::::::::::::::
df %>% wilcox_test (len ~ supp, paired = TRUE)

# Compare supp levels after grouping the data by "dose"
#::::::::::::::::::::::::::::::::::::::::
df %>%
  group_by(dose) %>%
  wilcox_test(data =., len ~ supp) %>%
  adjust_pvalue(method = "bonferroni") %>%
  add_significance("p.adj")

# pairwise comparisons
#::::::::::::::::::::::::::::::::::::::::
# As dose contains more than two levels ==>
# pairwise test is automatically performed.
df %>% wilcox_test(len ~ dose)

# Comparison against reference group
#::::::::::::::::::::::::::::::::::::::::
# each level is compared to the ref group
df %>% wilcox_test(len ~ dose, ref.group = "0.5")

# Comparison against all
#::::::::::::::::::::::::::::::::::::::::
df %>% wilcox_test(len ~ dose, ref.group = "all")

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