I am analizing pupil size data using mixed model analysis in R. I use lme()
from package nlme. However, I am encountering serious problems of heteroscedasticity and violation of normality assumption.
Do you have any suggestions on how to solve this problem?
I would like to test whether active neurostimulation (vs. control; Stimulation) increases pupil size over time (before stimulation vs. end stimulation; Time). We employed a between subject design.
I adopted the following strategies that did not solve the problem: 1. transformed the outcome - log(). 2. modelled the covariance-variance matrix following the instructions in this page https://rpsychologist.com/r-guide-longitudinal-lme-lmer#heteroscedasticity-at-level-1 .
Here the models: Here the models: Time (0 vs . 1) Stimulation (0 vs 1) Tonic_PS = pupil size ID = participant ID
model.1 <- lme(Tonic_PS ~ Time * Stimulation,,
random = ~ Time|ID,
weights = varIdent(form= ~ 1 | Stimulation),
data =Tonic_pupilsize_T2,
method = "ML")
model.2 <- lme(Tonic_PS ~ Time * Stimulation,
random = ~ Time|ID,
weights = varIdent(form= ~ 1 | Stimulation * Time),
data =Tonic_pupilsize_T2,
method = "ML")
model.3 <- lme(Tonic_PS ~ Time * Stimulation,
random = ~ Time|ID,
correlation = corAR1(),
data =Tonic_pupilsize_T2,
method = "ML")
model.4 <- lme(Tonic_PS ~ Time * Stimulation, random = ~ Time|ID,
weights= varIdent(form= ~ 1 | Time),
correlation = corAR1(),
data =Tonic_pupilsize_T2,
method = "ML")
Any suggestion will be very appreciated!