I am using the R package nlme to perform a repeated measure analysis. The model call is
mymodel<-lme(measure~time, data=mydata[is.finite(mydata$measure),], random= ~ 1 | subjectId/time). I would like to provide an estimate of the standard error of residual, by means the residual after the time effect (the pure random residual). When observing the model call I get:
Linear mixed-effects model fit by REML Data: mydata[is.finite(mydata$measure), ] AIC BIC logLik 182 190 -83.9 Random effects: Formula: ~1 | subjectId (Intercept) StdDev: 4.42 Formula: ~1 | time %in% subjectId (Intercept) Residual StdDev: 4.43 1.89 Fixed effects: measure ~ time Value Std.Error DF t-value p-value (Intercept) 13.80 2.07 16 6.68 0.000 timePost -1.44 2.34 16 -0.61 0.548 timePost 2 mesi -2.21 2.59 16 -0.85 0.406 timePost 4 mesi -2.31 2.77 16 -0.84 0.416 Correlation: (Intr) timPst tmPs2m timePost -0.479 timePost 2 mesi -0.433 0.426 timePost 4 mesi -0.405 0.398 0.369 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -0.5487 -0.1739 -0.0217 0.2025 0.6455 Number of Observations: 29 Number of Groups: subjectId time %in% subjectId 10 29
So i find two measures of residuals: 4.42, 4.43/1.89, that appears different from the one I get calling
sd(residuals(mymodel)) that In my basing intuition is the traditional meausre of residual. So my question is twofolds:
- What are the different type of residuals in nlme/lme4 or in mixed effects modeling in general?
- How can I get the various ones?
- How can I get the residuals after any modelable effects (fixed or random)? I would use
sd(residuals(mymodel))but I am not sure.