kruskal_wallis_test: Kruskal-Wallis test
In sjstats: Collection of Convenient Functions for Common Statistical Computations

kruskal_wallis_test

R Documentation

Kruskal-Wallis test

Description

This function performs a Kruskal-Wallis rank sum test, which is a non-parametric method to test the null hypothesis that the population median of all of the groups are equal. The alternative is that they differ in at least one. Unlike the underlying base R function kruskal.test(), this function allows for weighted tests.

Usage

kruskal_wallis_test(data, select = NULL, by = NULL, weights = NULL)

Arguments

`data`	A data frame.
`select`	Name(s) of the continuous variable(s) (as character vector) to be used as samples for the test. `select` can be one of the following: `select` can be used in combination with `by`, in which case `select` is the name of the continous variable (and `by` indicates a grouping factor). `select` can also be a character vector of length two or more (more than two names only apply to `kruskal_wallis_test()`), in which case the two continuous variables are treated as samples to be compared. `by` must be `NULL` in this case. If `select` select is of length two and `paired = TRUE`, the two samples are considered as dependent and a paired test is carried out. If `select` specifies one variable and `by = NULL`, a one-sample test is carried out (only applicable for `t_test()` and `wilcoxon_test()`) For `chi_squared_test()`, if `select` specifies one variable and both `by` and `probabilities` are `NULL`, a one-sample test against given probabilities is automatically conducted, with equal probabilities for each level of `select`.
`by`	Name of the variable indicating the groups. Required if `select` specifies only one variable that contains all samples to be compared in the test. If `by` is not a factor, it will be coerced to a factor. For `chi_squared_test()`, if `probabilities` is provided, `by` must be `NULL`.
`weights`	Name of an (optional) weighting variable to be used for the test.

Details

The function simply is a wrapper around kruskal.test(). The weighted version of the Kruskal-Wallis test is based on the survey package, using survey::svyranktest().

Value

A data frame with test results.

Which test to use

The following table provides an overview of which test to use for different types of data. The choice of test depends on the scale of the outcome variable and the number of samples to compare.

Samples	Scale of Outcome	Significance Test
1	binary / nominal	`chi_squared_test()`
1	continuous, not normal	`wilcoxon_test()`
1	continuous, normal	`t_test()`
2, independent	binary / nominal	`chi_squared_test()`
2, independent	continuous, not normal	`mann_whitney_test()`
2, independent	continuous, normal	`t_test()`
2, dependent	binary (only 2x2)	`chi_squared_test(paired=TRUE)`
2, dependent	continuous, not normal	`wilcoxon_test()`
2, dependent	continuous, normal	`t_test(paired=TRUE)`
>2, independent	continuous, not normal	`kruskal_wallis_test()`
>2, independent	continuous, normal	`datawizard::means_by_group()`
>2, dependent	continuous, not normal	not yet implemented (1)
>2, dependent	continuous, normal	not yet implemented (2)

(1) More than two dependent samples are considered as repeated measurements. For ordinal or not-normally distributed outcomes, these samples are usually tested using a friedman.test(), which requires the samples in one variable, the groups to compare in another variable, and a third variable indicating the repeated measurements (subject IDs).

(2) More than two dependent samples are considered as repeated measurements. For normally distributed outcomes, these samples are usually tested using a ANOVA for repeated measurements. A more sophisticated approach would be using a linear mixed model.

References

Bender, R., Lange, S., Ziegler, A. Wichtige Signifikanztests. Dtsch Med Wochenschr 2007; 132: e24–e25
du Prel, J.B., Röhrig, B., Hommel, G., Blettner, M. Auswahl statistischer Testverfahren. Dtsch Arztebl Int 2010; 107(19): 343–8

Examples

data(efc)
# Kruskal-Wallis test for elder's age by education
kruskal_wallis_test(efc, "e17age", by = "c172code")

# when data is in wide-format, specify all relevant continuous
# variables in `select` and omit `by`
set.seed(123)
wide_data <- data.frame(
  scale1 = runif(20),
  scale2 = runif(20),
  scale3 = runif(20)
)
kruskal_wallis_test(wide_data, select = c("scale1", "scale2", "scale3"))

# same as if we had data in long format, with grouping variable
long_data <- data.frame(
  scales = c(wide_data$scale1, wide_data$scale2, wide_data$scale3),
  groups = rep(c("A", "B", "C"), each = 20)
)
kruskal_wallis_test(long_data, select = "scales", by = "groups")
# base R equivalent
kruskal.test(scales ~ groups, data = long_data)

sjstats documentation built on May 29, 2024, 12:09 p.m.