Dear all I am fitting and analyzing linear mixed-effects models using the R command 'lme'. The following is the results:
dental.fit <- lme(fixed = distance~age, random = ~age + cluster = ~subject, data = dental) > summary(dental.fit) Variance/Covariance Components Estimates: Standard Deviation(s) of Random Effect(s) (Intercept) age 2.134464 0.1541247 Correlation of Random Effects (Intercept) age -0.6024329 Cluster Residual Variance: 1.716232 Fixed Effects Estimates: Value Approx. Std.Error z ratio(C) (Intercept) 16.3406250 0.98005731 16.6731321 age 0.7843750 0.08275189 9.4786353 sex 1.0321023 1.53545472 0.6721802 age:sex -0.3048295 0.12964730 -2.3512218 Conditional Correlations of Fixed Effects Estimates (Intercept) age sex age -0.8801554 sex -0.6382847 0.5617897 age:sex 0.5617897 -0.6382847 -0.8801554 I have known that using command 'dental.fit$varFix' I can obtain the conditional covariance matrix of the fixed effects. My question is how I can return the covariance matrix estimate of the random effects. I tried many commands such as 'dental.fit$varRan', 'dental.fit$var.Ran', but they didn't work. Thanks very much! Chaofeng ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.