I am caught in a mental trap. Why isn't the between groups variance estimated (0.0038) to be around the value with which I generated the data (0.0002)?
Thanks Toby set.seed(76589437887) fph = 0.4 Sigh = sqrt(0.0002) Sigi = sqrt(0.04) ci = 1 fpi = matrix(,7200,3) for (i in 1:90) { fph = rnorm(1, fph, Sigh) for (k in 1:80) { fpi[ci,1:3] = matrix(c(i, k, rnorm(1, fph, Sigi)),1) ci = ci+1 } } colnames(fpi) = c("hospid", "empid", "fpi1") dta = as.data.frame(fpi) lme = lme(fpi1 ~ 1, dta, ~1|hospid) summary(lme) lme = lme(fpi1 ~ 1, dta, ~1|hospid) summary(lme) Linear mixed-effects model fit by REML Data: dta AIC BIC logLik -2555.416 -2534.771 1280.708 Random effects: Formula: ~1 | hospid (Intercept) Residual StdDev: 0.06173257 0.1997302 Fixed effects: fpi1 ~ 1 Value Std.Error DF t-value p-value (Intercept) 0.368082 0.006919828 7110 53.19236 0 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -3.4870696 -0.6747173 -0.0048658 0.6838012 4.2633384 Number of Observations: 7200 Number of Groups: 90 0.0617^2 [1] 0.00380689 ______________________________________________ R-help@r-project.org 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.