I simulated some random data and didnt have any 'performance' issues.
library(lme4)
library(tidyverse)
mydata1 <-
expand.grid(
mood=c("sad","happy"),
language=c("English","Polish"),
valence=c("neutral","positive","negative")
)
set.seed(42)
mydata1$random_mean <-runif(nrow(mydata1),max=10)
mydata1$random_sd <-runif(nrow(mydata1),max=10)
#double it 5times
mydata<-union_all(mydata1,mydata1)
mydata<-union_all(mydata,mydata)
mydata<-union_all(mydata,mydata)
mydata<-union_all(mydata,mydata)
mydata<-union_all(mydata,mydata)
# make probablistic results
mydata<-mutate(mydata,
Errors = round(pmax(rnorm(random_mean,random_sd),0),digits = 0),
rt = round(pmax(rnorm(random_mean,random_sd),0),digits = 2)*500) %>%
select(-random_mean,random_sd)
mod1<-lme4::lmer(formula=
Errors ~ valence + language | mood ,
data = mydata)
mydata$prederrors<-predict(mod1,newdata=mydata)
to_plot <- mydata %>% mutate(moodlanguage=paste0(mood,language)) %>%
select(moodlanguage,valence,Errors,prederrors)
to_plot <- pivot_longer(to_plot,
cols= c("Errors","prederrors"),
names_to="category",
values_to="value")
ggplot(to_plot,
mapping = aes(x=moodlanguage,y=value,color=valence,shape=category)) +
geom_point() + facet_wrap(~category)