この節の作者: 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
# -------------------------------
#