Provides a pipe-friendly framework to performs one and two sample Wilcoxon tests. Read more: Wilcoxon in R.
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,
id = NULL,
error.as.na = FALSE
)
pairwise_wilcox_test(
data,
formula,
comparisons = NULL,
ref.group = NULL,
p.adjust.method = "holm",
detailed = FALSE,
...
)a data.frame containing the variables in the 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.
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"))
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).
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".
a logical indicating whether you want a paired test.
a logical indicating whether an exact p-value should be computed.
a character string specifying the alternative
hypothesis, must be one of "two.sided" (default),
"greater" or "less". You can specify just the initial
letter.
a number specifying an optional parameter used to form the null hypothesis.
confidence level of the interval.
logical value. Default is FALSE. If TRUE, a detailed result is shown.
(optional) character string specifying the column that contains the
sample/subject identifier, used only for a paired test
(paired = TRUE). When supplied, observations of the two compared
groups are matched by id (instead of by row order), and only subjects
present in both groups are used. For more than two groups, the matching is
done independently for each pairwise comparison, so different comparisons can
be based on different numbers of pairs (per-comparison pairwise deletion).
This makes paired tests work when some observations are missing or the groups
have unequal sizes. The default (id = NULL) keeps the previous
behaviour (groups paired in row order).
logical. If TRUE, a comparison that cannot be
computed (for example a group with fewer than two observations, or data that
are essentially constant) returns an NA result row with a warning
instead of stopping with an error; the other comparisons (or groups, for a
grouped analysis) are still computed. Default is FALSE (the comparison
errors as before).
other arguments to be passed to the function
wilcox.test.
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.
- 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.
wilcox_test(): Wilcoxon test
pairwise_wilcox_test(): performs pairwise two sample Wilcoxon test.
When a ref.group is specified, the reference group is taken as
group1 and the other group as group2, and the comparison is
computed as group1 versus group2 (i.e. ref.group versus
the other group), following the wilcox.test convention.
With detailed = TRUE, the estimate is the Hodges-Lehmann
location shift of group1 relative to group2, so a positive
estimate means the reference group is shifted higher; flip its sign
(mutate(estimate = -estimate)) if you want a positive sign to mean
"higher in the non-reference group". (The statistic is the
rank-sum/signed-rank W, which is not a signed difference.)
# Load data
#:::::::::::::::::::::::::::::::::::::::
data("ToothGrowth")
df <- ToothGrowth
# One-sample test
#:::::::::::::::::::::::::::::::::::::::::
df %>% wilcox_test(len ~ 1, mu = 0)
#> Error in UseMethod("wilcox_test"): no applicable method for 'wilcox_test' applied to an object of class "data.frame"
# Two-samples unpaired test
#:::::::::::::::::::::::::::::::::::::::::
df %>% wilcox_test(len ~ supp)
#> Error in UseMethod("wilcox_test"): no applicable method for 'wilcox_test' applied to an object of class "data.frame"
# Two-samples paired test
#:::::::::::::::::::::::::::::::::::::::::
df %>% wilcox_test (len ~ supp, paired = TRUE)
#> Error in UseMethod("wilcox_test"): no applicable method for 'wilcox_test' applied to an object of class "data.frame"
# 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")
#> Error in add_significance(., "p.adj"): The column p.adj does not exist in the data
# pairwise comparisons
#::::::::::::::::::::::::::::::::::::::::
# As dose contains more than two levels ==>
# pairwise test is automatically performed.
df %>% wilcox_test(len ~ dose)
#> Error in UseMethod("wilcox_test"): no applicable method for 'wilcox_test' applied to an object of class "data.frame"
# Comparison against reference group
#::::::::::::::::::::::::::::::::::::::::
# each level is compared to the ref group
df %>% wilcox_test(len ~ dose, ref.group = "0.5")
#> Error in UseMethod("wilcox_test"): no applicable method for 'wilcox_test' applied to an object of class "data.frame"
# Comparison against all
#::::::::::::::::::::::::::::::::::::::::
df %>% wilcox_test(len ~ dose, ref.group = "all")
#> Error in UseMethod("wilcox_test"): no applicable method for 'wilcox_test' applied to an object of class "data.frame"