Running analysis of variance (aov) across multiple grouping variables.

grouped_aov(
  data,
  grouping.vars,
  formula,
  effsize = "eta",
  output = "tidy",
  ...
)

Arguments

data

A data frame in which the variables specified in the formula will be found. If missing, the variables are searched for in the standard way.

grouping.vars

Grouping variables.

formula

A formula specifying the model.

effsize

Character describing the effect size to be displayed: "eta" (default) or "omega".

output

A character describing what output is expected. Two possible options: "tidy" (default), which will return the results, or "tukey", which will return results from Tukey's Honest Significant Differences method for post hoc comparisons. The "glance" method to get model summary is currently not supported for this function.

...

Currently ignored.

Examples

# uses dataset included in the `groupedstats` package set.seed(123) library(groupedstats) # effect size groupedstats::grouped_aov( formula = wt ~ mpg, data = mtcars, grouping.vars = am, effsize = "eta" )
#> # A tibble: 2 x 11 #> am term F.value df1 df2 p.value eta.sq.partial conf.level conf.low #> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 mpg 52.3 1 11 0.0000169 0.826 0.95 0.543 #> 2 0 mpg 24.4 1 17 0.000125 0.589 0.95 0.243 #> conf.high significance #> <dbl> <chr> #> 1 0.913 *** #> 2 0.766 ***