# How to disaggregate level 1 residuals in lmer models in r

I am running a linear mixed model in r:

 model <- lmer(variable ~  time +(1+time|id), data = long)


The output for random effects is:

Random effects:
Groups   Name        Variance   Std.Dev. Corr
id       (Intercept) 0.14163958 0.376350
time        0.00008384 0.009157 0.39
Residual             0.01127142 0.106167
Number of obs: 842, groups:  id, 250


I was wondering how to disaggregate the residual terms to show random effect residuals at each time point. In comparison, Mplus output automatically produces what we wanted:

MODEL RESULTS

Two-Tailed
Estimate       S.E.  Est./S.E.    P-Value

Residual Variances
N01                0.010      0.003      3.704      0.000
N02                0.012      0.002      5.021      0.000
N03                0.012      0.002      5.951      0.000
N04                0.009      0.003      3.352      0.001



It appears that the residual term in R is simply an average of the 4 residual terms in Mplus. Is there a way to split up the R residual to each time point, and obtaining similar output to Mplus? Thank you!

N01 [a];

Then run a likelihood ratio test comparing the two models, either in Mplus or in R (check out SBSDiff in R if you are using type=MLR in MPlus). If the model with the equivalent residual variances fits the data as well as the model with unique residual variances, then the mixed model approach in lmer is perfectly acceptable.
If you wanted to use R to get unique residual variances, you will have to switch over to nlme or lavaan. For examples with nlme, see this extremely helpful page.