Dear useRs, I am working on a series of field experiments (159 in total) carried out in different years in several locations. The cultivars in each experiment are not always the same, in fact they change over time. I would like to get the fitted values and MSE of the individual fits from the following lmList object, so I can use them to fit a mixed model using the fitted values and weight each environment with (number of reps/MSE)/Average MSE. library(nlme) yield.list <- lmList(yield ~ as.factor(rep) + id | yearloc, data = df1, na.action = na.omit) then I do yield.fitted <- fitted(yield.list) yield.fitted Akron1999 Akron1999 Akron1999 Akron1999 Akron1999 45.06508 61.29841 63.73175 61.16508 63.53175 Akron1999 Akron1999 Akron1999 Akron1999 Akron1999 64.63175 53.09841 60.66508 70.36508 57.19841 Akron1999 Akron1999 Akron1999 Akron1999 Akron1999 55.33175 60.79841 54.79841 56.46508 64.99841 Akron1999 Akron1999 Akron1999 Akron1999 Akron1999 57.56508 58.59841 57.86508 56.43175 51.79841 The label of each value is the "yearloc", I also need to know their "id". Is there a way to ad this info? I have not figured out how to obtain individual mean square errors from an lmList object. However, I can do it with: res <- as.data.frame(matrix(NA, ncol = 3, nrow = length(levels(yieldh$yearloc)), dimnames = list(NULL, c("yearloc", "MSE", "rep")))) for (i in 1:length(levels(yieldh$yearloc))) { dta <- subset(yieldh,yieldh$yearloc==levels(yieldh$yearloc)[i], drop = T) x <- anova(lm(yield ~ factor(rep) + id, data = dta, na.actio = na.omit))$"Mean Sq"[3] res[i,] <- c(levels(yieldh$yearloc)[i],x, length(levels(dta$rep))) } Can anybody give me directions to solve this? Thanks, Marc [[alternative HTML version deleted]]