Hi Everyone,
I have a question about Mixed-Design Anova in R. I want to obtain Mauchly�s
test of Sphericity and the Greenhouse-Geisser correction. I have managed to do
it in SPSS:
GLM Measure1 Measure2 Measure3 Measure4 Measure5 Measure6 BY Grouping
/WSFACTOR=Measure 6 Polynomial
/METHOD=SSTYPE(3)
/PLOT=PROFILE(Measure*Grouping)
/CRITERIA=ALPHA(.05)
/WSDESIGN=Measure
/DESIGN=Grouping.
I have tried to replicate this in R:
library("dplyr")
library("tidyr")
library("ggplot2")
library("ez")
PatientID <- c(1:10)
Measure1 <- c(3,5,7,4,NA,7,4,4,7,2)
Measure2 <- c(1,2,5,6,8,9,5,NA,6,7)
Measure3 <- c(3,3,5,7,NA,4,5,7,8,1)
Measure4 <- c(1,2,5,NA,3,NA,6,7,3,6)
Measure5 <- c(2,3,NA,8,3,5,6,3,6,4)
Measure6 <- c(1,2,4,6,8,3,5,6,NA,4)
Grouping <- c(1,0,1,1,1,0,0,1,1,0)
dataframe <- data.frame(PatientID, Measure1, Measure2, Measure3, Measure4,
Measure5, Measure6, Grouping)
dataframe$Grouping <- as.factor(dataframe$Grouping)
dataframe
ezPrecis(dataframe)
glimpse(dataframe)
dataframe %>% count(PatientID)
dataframe %>% count(PatientID, Grouping, Measure1, Measure2, Measure3,
Measure4, Measure5, Measure6) %>%
filter(PatientID %in% c(1:243)) %>%
print(n = 10)
# So, we have a mixed design with one between factor (Grouping) and 6 within
factors (Measure 1 to 6).
dat_means <- dataframe %>%
group_by(Grouping, Measure1, Measure2, Measure3, Measure4, Measure5,
Measure6) %>%
summarise(mRT = mean(c(Measure1, Measure2, Measure3, Measure4, Measure5,
Measure6))) %>% ungroup()
View(dat_means)
ggplot(dat_means, aes(c(Measure1, Measure2, Measure3, Measure4, Measure5,
Measure6), mRT, colour = Grouping)) +
geom_line(aes(group = Grouping)) +
geom_point(aes(shape = Grouping), size = 3) +
facet_wrap(~group)
ANOVA <- ezANOVA(dat, x, PatientID, within = .( c(Measure1, Measure2, Measure3,
Measure4, Measure5, Measure6)),
between = Grouping, type = 3)
print(ANOVA)
However, this does not work. I know I am probably doing it completely wrong,
but I do not know how to solve it. Besides that, I do not know what to fill in
at the �x�.
Can somebody help me?
Thank you in advance.
Lisa
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