I have a repeated measures data set with a response variable and two predictor variables (stimulus frequency and stimulus number). There are missing data, but it is only random for stimulus. Explained further, if I tested a given frequency in a subject, there is an observation for every possible stimulus number with that frequency, but not every frequency was tested in every subject. Due to the missing data, I can't run a repeated measures two-way ANOVA, so, as I understand it, I need to fit a linear mixed effects model.
My data are arranged in a table with 4 columns (Subject, Frequency, Stimulus, Response), with NaNs in the place of missing data. Additionally, I have reason to believe that there is an interaction between the predictors.
The formula that I'm currently using is:
lme = fitlme(mixedModelData,'Response ~ 1 + Stimulus*Frequency + (1|Subject)');
Does this model appropriately address the situation I have described?