search for: sub_noise

...as been done and, given its absence from the lmer output, refuted somewhere. A reference would be lovely. Code below. Many thanks, Andy ################################################################# require(lme4) # First generate some data people = 100 obs = 4 # The random intercepts: sub_noise = data.frame(id = 1:people, rand_int = rnorm(people,0,60)) # Merge everything id = rep(1:people,obs) thedata = data.frame(id) thedata = merge(sub_noise, thedata) thedata$x = rep(1:obs,people) # Setup the relationship between x and y thedata$y = 23*thedata$x + 20 + thedata$rand_int...

...ffects with random effects flattened to predict y's. Code below. Best wishes, Andy ############################################################## require(lme4) set.seed(0) # Data for 100 people, each of whom has 10 observations people = 100 obs = 10 # Generate person-level variation sub_noise = data.frame(id = 1:people, rand_int = rnorm(people,0,60)) # And within person noise resids = rnorm(people*obs, 0, 20) id = rep(1:people,obs) thedata = data.frame(id) thedata = merge(sub_noise, thedata) thedata$x = rep(1:obs,people) thedata$y = 23*thedata$x + 20 + thedata$rand_int + resids thed...