Thank you. On Sun, 2005-06-12 at 18:54 -0500, Douglas Bates wrote: > On 6/12/05, Adaikalavan Ramasamy <[EMAIL PROTECTED]> wrote: > > Thank you for confirming this and introducing me to varcomp(). > > > > I have another question that I hope you or someone else can help me > > with. I was trying to generalise my codes for variable measurement > > levels and discovered that lme() was estimating the within group > > variance even with a single measure per subject for all subjects ! > > > > Here is an example where we have 12 animals but with single measurement. > > > > y <- c(2.2, -1.4, -0.5, -0.3, -2.1, 1.5, > > 1.3, -0.3, 0.5, -1.4, -0.2, 1.8) > > ID <- factor( 1:12 ) > > > > > > Analysis of variance method correctly says that there is no residual > > variance and it equals to total variance. > > > > summary(aov(y ~ ID)) > > Df Sum Sq Mean Sq > > ID 11 20.9692 1.9063 > > > > > > However the REML method is giving me a within animal variance when there > > is no replication at animal level. It seems like I can get components of > > variance for factors that are not replicated. > > > > library(ape) > > varcomp(lme(y ~ 1, random = ~ 1 | ID)) > > ID Within > > 1.6712661 0.2350218 > > > > Am I reading this correct and can someone kindly explain this to me ? > > It's a spurious convergence in lme. There is no check in lme for the > number of observations exceeding the number of groups. There should > be. I'll add this to the bug reports list. >
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