Is it possible to overfit a model by virtue of having too many categorical variables?
I have 3 categorical variables and my dependent measure is continuous (or a ratio I guess, I'm measuring accuracy/error rates).
mod4 <- lmer(accuracy ~ group + session + trialtype + session:trialtype + (1 | subject), REML = FALSE, data = data) mod5 <- lmer(accuracy ~ group + session + trialtype + session:trialtype + (trialtype | subject), REML = FALSE, data = data)
mod 4 I don't get any warning or error, and
mod 5 I get the
boundary (singular) fit message. I've included the
trialtype as a random slope to replicate previous data.
When I've done an ANOVA on the two models,
mod4 has lower AIC.
I have 3 groups, 30 subjects in each group. Each subjects does a pre- and post-test task. In this task, there are 4 trial types. So I have 8 observations (4 trials, 2 session) per subject. There is complete date, nothing missing. Also, accuracy is being measured 0-1. No subjects have accuracy of 0. So they're ratios.