AObootMixed {AOboot} | R Documentation |
In case of violations of the assumption of the normal distribution, researchers
usually employ bootstrapping. Based on the R
packages
afex and
emmeans, this function
computes bootstrapped confidence intervals for the effect sizes, estimated
marginal means, and post hoc tests for Mixed ANOVAs. Furthermore, the p-values
of the F-statistic are adjusted to reflect the probability to obtain equal or
higher values than the raw, non-bootstrapped ANOVA (Stine, 1989 <doi:10.1177/0049124189018002003>; see
also this tutorial by Nadine Spychala.).
AObootMixed(var.within,
var.between,
var.id,
levels.w1,
levels.b1,
eff.si = c("pes", "ges"),
data,
silence = FALSE,
n.sim = 1000,
alpha = .05,
seed = 1234,
n.round = 2)
var.within |
Variable(s) reflecting the within-subject level. |
var.between |
Variable(s) reflecting the between-subject level. |
var.id |
Unique person specifier. |
levels.w1 |
Levels of the within-subjects variable. Must be identical with the levels in the dataset. |
levels.b1 |
Levels of the between-subjects variable. Must be identical with the levels in the dataset. |
eff.si |
Effect size for the F-tests. |
data |
Name of the dataframe. The dataset must be in a wide-format, with one row per participant. |
silence |
Logical. If FALSE, progress of the bootstrapping procedure will be displayed. |
n.sim |
Number of bootstrap samples to be drawn. |
alpha |
Type I error. |
seed |
To make the results reproducible, it is recommended to set a random seed parameter. |
n.round |
Number of digits in the output. |
The p-value of the F-test (Pr(>F)
) in the output reflects the
probability to obtain an F-value as high as or higher than the F-value from the
raw, non-bootstrapped ANOVA. Thus, it should not be mistaken as a p-value in the
sense of a null hypothesis significance test. More information about this can be
found in this tutorial by Nadine Spychala.
type.aov |
Type of ANOVA conducted. |
factor1 |
Name of the groups in the between factor. |
factor2 |
Name of the groups in the within factor. |
anova |
Results of the conducted ANOVA (i.e., degrees of freedom, F-test, p-value, effect size with bootstrap confidence interval, and numbers of tests for which convergence was achieved. |
em.1 |
Estimated marginal means for between factor. |
em.2 |
Estimated marginal means for within factor. |
em.3 |
Estimated marginal means for between factor by within factor. |
em.4 |
Estimated marginal means for within factor by between factor. |
no.test1 |
Number of post hoc tests for the between factor for which convergence was achieved. |
no.test2 |
Number of post hoc tests for the within factor for which convergence was achieved. |
no.test3 |
Number of post hoc tests for the between factor by within factor for which convergence was achieved. |
no.test4 |
Number of post hoc tests for within factor by between factor for which convergence was achieved. |
ph.1 |
Post hoc tests for between factor. |
ph.2 |
Post hoc tests for within factor. |
ph.3 |
Post hoc tests for between factor by within factor. |
ph.4 |
Post hoc tests for within factor by between factor. |
output <- list(type.aov = "Two-way mixed ANOVA", factor1 = levels.b1, factor2 = levels.w1, anova = round(orig.aov$anova_table, n.round), em.1 = dat.em1, no.test1 = no.test1, ph.1 = dat.ph1, em.2 = dat.em2, no.test2 = no.test2, ph.2 = dat.ph2, em.3 = dat.em3, no.test3 = no.test3, ph.3 = dat.ph3, em.4 = dat.em4, no.test4 = no.test4, ph.4 = dat.ph4)
Lisa-Marie Segbert, Christian Blötner c.bloetner@gmail.com
Stine, R. (1989). An introduction to bootstrap methods: Examples and ideas. Sociological Methods & Research, 18(2-3), 243–291. <https://doi.org/10.1177/0049124189018002003>
library(carData)
# The OBrienKaiser dataset from the carData package
ao <- OBrienKaiser
# Add a unique person identifier to the dataset
ao$pers <- 1:nrow(OBrienKaiser)
# Mixed ANOVA
AObootMixed(
var.within = c("pre.1", "post.1", "fup.1"),
var.between = "treatment",
var.id = "pers",
levels.w1 = c("pre", "post", "fup"),
levels.b1 = c("control", "A", "B"),
eff.si = "pes",
data = ao,
n.sim = 1000,
alpha = .05,
seed = 1234,
n.round = 2)