I hope you all don't mind this question, but I need help interpreting output for a linear mixed effects model output I've been trying to learn to do in R. I am new to longitudinal data analysis and linear mixed effects regression. I have a model I fitted with weeks as the time predictor, and score on an employment course as my outcome. I modeled score with weeks (time) and several fixed effects, sex and race. My model includes random effects. I need help understanding what the variance and correlation means. The output is the following:
Random effects Group Name Variance EmpId intercept 680.236 weeks 13.562 Residual 774.256
The correlaton is .231.
I can interpret the correlation as there is a a positive relationship between weeks and score but I want to be able to say it in terms of "23% of ...".
I really appreciate the help.
Thanks "guest" and Macro for replying. Sorry, for not replying, I was out at a conference and I’m now catching up. Here is the output and the context.
Here is the summary for the LMER model I ran.
>summary(LMER.EduA) Linear mixed model fit by maximum likelihood Formula: Score ~ Weeks + (1 + Weeks | EmpID) Data: emp.LMER4 AIC BIC logLik deviance REMLdev 1815 1834 -732.6 1693 1685 Random effects: Groups Name Variance Std.Dev. Corr EmpID (Intercept) 680.236 26.08133 Weeks 13.562 3.682662 0.231 Residual 774.256 27.82546 Number of obs: 174, groups: EmpID, 18 Fixed effects: Estimate Std. Error t value (Intercept) 261.171 6.23 37.25 Weeks 11.151 1.780 6.93 Correlation of Fixed Effects: (Intr) Days -0.101
I don’t understand how to interpret the variance and residual for the random effects and explain it to someone else. I also don’t know how to interpret the correlation, other than it is positive which indicates that those with higher intercepts have higher slopes and those with those with lower intercepts have lower slopes but I don’t know how to explain the correlation in terms of 23% of . . . . (I don’t know how to finish the sentence or even if it makes sense to do so). This is a different type analysis for us as we (me) are trying to move into longitudinal analyses.
I hope this helps.
Thanks for your help so far.