Section author: Ravi Selker, Jonathon Love, Damian Dropmann

One-Way ANOVA (non-parametric; anovaNP)

Description

The Kruskal-Wallis test is used to explore the relationship between a continuous dependent variable, and a categorical explanatory variable. It is analagous to ANOVA, but with the advantage of being non-parametric and having fewer assumptions. However, it has the limitation that it can only test a single explanatory variable at a time.

Usage

anovaNP(
    data,
    deps,
    group,
    es = FALSE,
    pairs = FALSE,
    formula
)

Arguments

data	the data as a data frame
deps	a string naming the dependent variable in data
group	a string naming the grouping or independent variable in data
es	TRUE or FALSE (default), provide effect-sizes
pairs	TRUE or FALSE (default), perform pairwise comparisons
formula	(optional) the formula to use, see the examples

Output

A results object containing:

results$table	a table of the test results
results$comparisons	an array of pairwise comparison tables

Tables can be converted to data frames with asDF or as.data.frame(). For example:

results$table$asDF

as.data.frame(results$table)

Examples

data('ToothGrowth')

anovaNP(formula = len ~ dose, data=ToothGrowth)

#
#  ONE-WAY ANOVA (NON-PARAMETRIC)
#
#  Kruskal-Wallis
#  -------------------------------
#           X²      df    p
#  -------------------------------
#    len    40.7     2    < .001
#  -------------------------------
#