# setup set.seed ( 123 ) # tidy data as output ggcorrmat (ĭata = dplyr :: select ( ggplot2 :: msleep, dplyr :: matches ( "sleep|awake" ) ), (Algina-Keselman-Penfield robust standardized difference)įor more, see the ggwithinstats vignette: įor more, including information about the variant of this function grouped_gghistostats, see the gghistostats vignette:
(Algina-Keselman-Penfield robust standardized difference average)
Yuen’s test on trimmed means for dependent samples Heteroscedastic one-way repeated measures ANOVA for trimmed means We will see an example with only repeated measurements. The central tendency measure displayed will depend on the statistics: TypeĪs with the ggbetweenstats, this function also has a grouped_ variant that makes repeating the same analysis across a single grouping variable quicker. Ggtheme = ggthemes :: theme_fivethirtyeight ( ) ) # for reproducibility and data set.seed ( 123 ) library ( WRS2 ) # for data library ( afex ) # to run anova # plot ggwithinstats (Ĭaption = "Data source: `WRS2` R package", Heteroscedastic one-way ANOVA for trimmed meansĮffectsize::omega_squared, effectsize::eta_squaredĮffectsize::cohens_d, effectsize::hedges_gįor more, see the ggbetweenstats vignette:
MAP (maximum a posteriori probability) estimate
0 kommentar(er)
