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...ata$x * coef(lm1)[2] plot(thedata$y, predicted) # It's a noisy mess cor(thedata$y, predicted)^2 # Now how about adjusting everyone's intercept towards the group # intercept? lmer1 = lmer(y ~ x + (1|id), data=thedata) summary(lmer1) # Get the random intercepts and stick them in a table ran_effects = data.frame(rownames(ranef(lmer1)$id), ranef(lmer1)$id[1]) names(ran_effects) = c("id", "b") ran_effects_data = merge(thedata, ran_effects) # Now compute predicted.ml = fixef(lmer1)[1] + ran_effects_data$x * fixef(lmer1)[2] + ran_effects_data$b plot(thedat...

...edata = merge(sub_noise, thedata) thedata$x = rep(1:obs,people) thedata$y = 23*thedata$x + 20 + thedata$rand_int + resids thedata$y.flat = 23*thedata$x + 20 + resids # Fit a random intercept model lmer1 = lmer(y ~ x + (1|id), data=thedata) summary(lmer1) # Get the intercepts ranef(lmer1)$id[1] ran_effects = data.frame(rownames(ranef(lmer1)$id), ranef(lmer1)$id[1]) names(ran_effects) = c("id", "b") ran_effects_data = merge(thedata, ran_effects) # Calculate the predicted y's using the fixed effects and flattening out # using the random effects: predicted.y = fixef(lmer1)[1]...