The emmeans for a mixed ANOVA model in the Rj Editor produces different results from those run in R. Here are the codes.
Code: Select all
id <- factor(c(1:30,1:30))
cond <- c("A","A","A","A","A","A","A","A","A","A",
"B","B","B","B","B","B","B","B","B","B",
"C","C","C","C","C","C","C","C","C","C",
"A","A","A","A","A","A","A","A","A","A",
"B","B","B","B","B","B","B","B","B","B",
"C","C","C","C","C","C","C","C","C","C")
time <- c(rep("baseline",30), rep("train",30))
y <- c( 8,5,3,5,2,6,5,6,4,4,
3,5,8,2,5,6,6,4,3,5,
3,5,8,5,5,6,6,6,3,4,
9,7,2,7,9,7,8,5,7,9,
5,5,10,5,3,10,9,5,7,5,
4,5,6,6,4,7,7,3,2,2)
a <- data.frame(id,cond,time,y)
# Fit Mixed ANOVA
fit <- aov(y ~ cond * time + Error(id/time), data = a)
summary(fit)
# EMMEANS
library("emmeans")
fit.emm <- emmeans(fit, ~ cond | time)
summary(fit.emm)
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##Error: id
## Df Sum Sq Mean Sq F value Pr(>F)
##cond 2 11.43 5.717 1.041 0.367
##Residuals 27 148.30 5.493
##
##Error: id:time
## Df Sum Sq Mean Sq F value Pr(>F)
##time 1 19.27 19.267 9.441 0.00481 **
##cond:time 2 20.63 10.317 5.055 0.01365 *
##Residuals 27 55.10 2.041
##---
##Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
##
##time = baseline:
## cond emmean SE df lower.CL upper.CL
## A 4.8 0.6137318 44.63 3.563598 6.036402
## B 4.7 0.6137318 44.63 3.463598 5.936402
## C 5.1 0.6137318 44.63 3.863598 6.336402
##
##time = train:
## cond emmean SE df lower.CL upper.CL
## A 7.0 0.6137318 44.63 5.763598 8.236402
## B 6.4 0.6137318 44.63 5.163598 7.636402
## C 4.6 0.6137318 44.63 3.363598 5.836402
##
##Confidence level used: 0.95
Best,
Kris