I am hoping to get some advice for examining differences in reaction times (repeated sampling) as a measure of cognitive load between groups.
Dataset: The response variable I am using is reaction times in ms relative to the mean(baseline RT) collected at multiple time points during a task. A small number (~.7%) includes negative reaction times given that RT = RT during task - mean baseline RT. Range of dataset is -125 to 3000 ms. Additionally, there are some missing response data points (177 out of 1764).
Plan: I am interested in using a generalized mixed-effect regression model (lme4::glmer) to examine differences between groups across time (fixed effects), with subjects as the random effects: RT ~ group:time + (1|subject). My plan is to use Inverse Gaussian GLMM with identity link function (as suggested in Lo and Andrews 2015).
However, I am running into two issues:
- First issue that my dataset includes negative response times and I am wondering if anyone has suggestions for how to deal with this? My thoughts are to shift the data by adding a constant (e.g., Y + min(Y)) however I am not sure if this is appropriate. Does anyone have any advice or articles on how to handle this?
- Even when dealing with all positive values (Y + min(Y)), I get the below error. I see that other people are also having this error with lme4::glmer but I haven't been able to find a clear reason for why this is happening other than it may be because of the NaN values. Does anyone know of the reasoning for this and/or any other packages to use for this type of analysis?
Error in (function (fr, X, reTrms, family, nAGQ = 1L, verbose = 0L, maxit = 100L, :
(maxstephalfit) PIRLS step-halvings failed to reduce deviance in pwrssUpdate
Any insights into issues 1 and/or 2 would be much appreciated!