```
##########################
## 1: Specifying ANOVAs ##
##########################
# Example using a purely within-subjects design
# (Maxwell & Delaney, 2004, Chapter 12, Table 12.5, p. 578):
data(md_12.1)
aov_ez("id", "rt", md_12.1, within = c("angle", "noise"),
anova_table=list(correction = "none", es = "none"))
# Default output
aov_ez("id", "rt", md_12.1, within = c("angle", "noise"))
# examples using obk.long (see ?obk.long), a long version of the OBrienKaiser
# dataset (car package). Data is a split-plot or mixed design: contains both
# within- and between-subjects factors.
data(obk.long, package = "afex")
# estimate mixed ANOVA on the full design:
aov_car(value ~ treatment * gender + Error(id/(phase*hour)),
data = obk.long, observed = "gender")
aov_4(value ~ treatment * gender + (phase*hour|id),
data = obk.long, observed = "gender")
aov_ez("id", "value", obk.long, between = c("treatment", "gender"),
within = c("phase", "hour"), observed = "gender")
# the three calls return the same ANOVA table:
# Anova Table (Type 3 tests)
#
# Response: value
# Effect df MSE F ges p.value
# 1 treatment 2, 10 22.81 3.94 + .198 .055
# 2 gender 1, 10 22.81 3.66 + .115 .085
# 3 treatment:gender 2, 10 22.81 2.86 .179 .104
# 4 phase 1.60, 15.99 5.02 16.13 *** .151 <.001
# 5 treatment:phase 3.20, 15.99 5.02 4.85 * .097 .013
# 6 gender:phase 1.60, 15.99 5.02 0.28 .003 .709
# 7 treatment:gender:phase 3.20, 15.99 5.02 0.64 .014 .612
# 8 hour 1.84, 18.41 3.39 16.69 *** .125 <.001
# 9 treatment:hour 3.68, 18.41 3.39 0.09 .002 .979
# 10 gender:hour 1.84, 18.41 3.39 0.45 .004 .628
# 11 treatment:gender:hour 3.68, 18.41 3.39 0.62 .011 .641
# 12 phase:hour 3.60, 35.96 2.67 1.18 .015 .335
# 13 treatment:phase:hour 7.19, 35.96 2.67 0.35 .009 .930
# 14 gender:phase:hour 3.60, 35.96 2.67 0.93 .012 .449
# 15 treatment:gender:phase:hour 7.19, 35.96 2.67 0.74 .019 .646
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
#
# Sphericity correction method: GG
# "numeric" variables are per default converted to factors (as long as factorize
# = TRUE):
obk.long$hour2 <- as.numeric(as.character(obk.long$hour))
# gives same results as calls before
aov_car(value ~ treatment * gender + Error(id/phase*hour2),
data = obk.long, observed = c("gender"))
# ANCOVA: adding a covariate (necessary to set factorize = FALSE)
aov_car(value ~ treatment * gender + age + Error(id/(phase*hour)),
data = obk.long, observed = c("gender", "age"), factorize = FALSE)
aov_4(value ~ treatment * gender + age + (phase*hour|id),
data = obk.long, observed = c("gender", "age"), factorize = FALSE)
aov_ez("id", "value", obk.long, between = c("treatment", "gender"),
within = c("phase", "hour"), covariate = "age",
observed = c("gender", "age"), factorize = FALSE)
# aggregating over one within-subjects factor (phase), with warning:
aov_car(value ~ treatment * gender + Error(id/hour), data = obk.long,
observed = "gender")
aov_ez("id", "value", obk.long, c("treatment", "gender"), "hour",
observed = "gender")
# aggregating over both within-subjects factors (again with warning),
# only between-subjects factors:
aov_car(value ~ treatment * gender + Error(id), data = obk.long,
observed = c("gender"))
aov_4(value ~ treatment * gender + (1|id), data = obk.long,
observed = c("gender"))
aov_ez("id", "value", obk.long, between = c("treatment", "gender"),
observed = "gender")
# only within-subject factors (ignoring between-subjects factors)
aov_car(value ~ Error(id/(phase*hour)), data = obk.long)
aov_4(value ~ (phase*hour|id), data = obk.long)
aov_ez("id", "value", obk.long, within = c("phase", "hour"))
### changing defaults of ANOVA table:
# no df-correction & partial eta-squared:
aov_car(value ~ treatment * gender + Error(id/(phase*hour)),
data = obk.long, anova_table = list(correction = "none", es = "pes"))
# no df-correction and no MSE
aov_car(value ~ treatment * gender + Error(id/(phase*hour)),
data = obk.long,observed = "gender",
anova_table = list(correction = "none", MSE = FALSE))
# add p-value adjustment for all effects (see Cramer et al., 2015, PB&R)
aov_ez("id", "value", obk.long, between = "treatment",
within = c("phase", "hour"),
anova_table = list(p_adjust_method = "holm"))
###########################
## 2: Follow-up Analysis ##
###########################
# use data as above
data(obk.long, package = "afex")
# 1. obtain afex_aov object:
a1 <- aov_ez("id", "value", obk.long, between = c("treatment", "gender"),
within = c("phase", "hour"), observed = "gender")
if (requireNamespace("ggplot2") & requireNamespace("emmeans")) {
# 1b. plot data using afex_plot function, for more see:
## vignette("afex_plot_introduction", package = "afex")
## default plot uses multivariate model-based CIs
afex_plot(a1, "hour", "gender", c("treatment", "phase"))
a1b <- aov_ez("id", "value", obk.long, between = c("treatment", "gender"),
within = c("phase", "hour"), observed = "gender",
include_aov = TRUE)
## you can use a univariate model and CIs if you refit the model with the aov
## slot
afex_plot(a1b, "hour", "gender", c("treatment", "phase"),
emmeans_arg = list(model = "univariate"))
## in a mixed between-within designs, no error-bars might be preferrable:
afex_plot(a1, "hour", "gender", c("treatment", "phase"), error = "none")
}
if (requireNamespace("emmeans")) {
library("emmeans") # package emmeans needs to be attached for follow-up tests.
# 2. obtain reference grid object (default uses multivariate model):
r1 <- emmeans(a1, ~treatment +phase)
r1
# 3. create list of contrasts on the reference grid:
c1 <- list(
A_B_pre = c(rep(0, 6), 0, -1, 1), # A versus B for pretest
A_B_comb = c(-0.5, 0.5, 0, -0.5, 0.5, 0, 0, 0, 0), # A vs. B for post and follow-up combined
effect_post = c(0, 0, 0, -1, 0.5, 0.5, 0, 0, 0), # control versus A&B post
effect_fup = c(-1, 0.5, 0.5, 0, 0, 0, 0, 0, 0), # control versus A&B follow-up
effect_comb = c(-0.5, 0.25, 0.25, -0.5, 0.25, 0.25, 0, 0, 0) # control versus A&B combined
)
# 4. test contrasts on reference grid:
contrast(r1, c1)
# same as before, but using Bonferroni-Holm correction for multiple testing:
contrast(r1, c1, adjust = "holm")
# 2. (alternative): all pairwise comparisons of treatment:
emmeans(a1, "treatment", contr = "pairwise")
}
#######################
## 3: Other examples ##
#######################
data(obk.long, package = "afex")
# replicating ?Anova using aov_car:
obk_anova <- aov_car(value ~ treatment * gender + Error(id/(phase*hour)),
data = obk.long, type = 2)
# in contrast to aov you do not need the within-subject factors outside Error()
str(obk_anova, 1, give.attr = FALSE)
# List of 5
# $ anova_table:Classes ‘anova’ and 'data.frame': 15 obs. of 6 variables:
# $ aov : NULL
# $ Anova :List of 14
# $ lm :List of 13
# $ data :List of 3
obk_anova$Anova
# Type II Repeated Measures MANOVA Tests: Pillai test statistic
# Df test stat approx F num Df den Df Pr(>F)
# (Intercept) 1 0.96954 318.34 1 10 6.532e-09 ***
# treatment 2 0.48092 4.63 2 10 0.0376868 *
# gender 1 0.20356 2.56 1 10 0.1409735
# treatment:gender 2 0.36350 2.86 2 10 0.1044692
# phase 1 0.85052 25.61 2 9 0.0001930 ***
# treatment:phase 2 0.68518 2.61 4 20 0.0667354 .
# gender:phase 1 0.04314 0.20 2 9 0.8199968
# treatment:gender:phase 2 0.31060 0.92 4 20 0.4721498
# hour 1 0.93468 25.04 4 7 0.0003043 ***
# treatment:hour 2 0.30144 0.35 8 16 0.9295212
# gender:hour 1 0.29274 0.72 4 7 0.6023742
# treatment:gender:hour 2 0.57022 0.80 8 16 0.6131884
# phase:hour 1 0.54958 0.46 8 3 0.8324517
# treatment:phase:hour 2 0.66367 0.25 16 8 0.9914415
# gender:phase:hour 1 0.69505 0.85 8 3 0.6202076
# treatment:gender:phase:hour 2 0.79277 0.33 16 8 0.9723693
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
```

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