I am finishing up some analysis on a large set of data. I would like to take the linear model used in the first part of the work and re-fit it using an linear mixed model (LME). The LME would be very similar with the exception that one of the variables used in the model would be used as a random effect. This data comes from many observations (>1000) in a small group of subjects (~10) and I know that modeling the effect of subject is better done as a random effect (this is a variable that I want to shift). The R code would look like:
my_modelB <- lm(formula = A ~ B + C + D) lme_model <- lme(fixed=A ~ B + C, random=~1|D, data=my_data, method='REML')
Everything runs fine and the results are vastly similar. It would be nice if I could use something like RLRsim or an AIC/BIC to compare these two models and decide which is the most appropriate. My colleagues don't want to report the LME because there isn't an easily accessible way of choosing which is "better", even though I think the LME is the more appropriate model. Any suggestions?