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
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