One-Way ANOVA (Non-parametric)
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.
Example usage
data('ToothGrowth')
anovaNP(formula = len ~ dose, data=ToothGrowth)
#
# ONE-WAY ANOVA (NON-PARAMETRIC)
#
# Kruskal-Wallis
# ───────────────────────────────
# χ² df p
# ───────────────────────────────
# len 40.7 2 < .001
# ───────────────────────────────
#
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 |
Returns
A results object containing:
| results$table | a table |
| results$comparisons | an array of tables |
Tables can be converted to data frames with asDF or as.data.frame(). For example:
results$table$asDF
as.data.frame(results$table)
Elements in arrays can be accessed with [[n]]. For example:
results$comparisons[[1]] # accesses the first element