This is an update to my previous post today after finding some previous posts about crossed random effects. Any comments would be much appreciated.
I have two random factors (Plot and Variety), one fixed factor (Time). I got rid of one of the fixed factors from my previous post for simplicity. Each Variety is tested at each of two Times, so I would like to include Time %in% Variety in my random factors. I tried the following based on a suggestion from Douglas Bates http://finzi.psych.upenn.edu/R/Rhelp02a/archive/26424.html > Data = groupedData(Measurement ~Time|Variety, data = read.table("mvone.txt",header = TRUE)) > Data$vt = factor(paste(Data$Variety,Data$Time,sep="/")) > Data$const=factor(rep(1,nrow(Data))) > Data.lme=lme(Measurement~Time,data=Data,random=list(const=pdBlocked(list(pdIdent(~Plot-1),pdIdent(~Variety-1),pdIdent(~vt-1))))) I'd like some help interpreting the results.(the ... mean that I deleted repetitive stuff) >summary(Data.lme) ... Block 1: Plot18Ea18La, Plot18Ea49Ea, Plot18Ea59Ea, Plot18Ea67Ea, Plot18La18Ea, Plot18La21La, Plot18La46La, Plot18La71La, Plot21Ea21La, ... Plot71La67La, Plot71La71Ea Formula: ~Plot - 1 | const Structure: Multiple of an Identity Plot18Ea18La Plot18Ea49Ea Plot18Ea59Ea Plot18Ea67Ea Plot18La18Ea StdDev: 0.973655 0.973655 0.973655 0.973655 0.973655 Plot18La21La Plot18La46La Plot18La71La Plot21Ea21La Plot21Ea59Ea StdDev: 0.973655 0.973655 0.973655 0.973655 0.973655 ... I take it that these StdDevs are the sqrt of the variance component for Plot. They are the diagonal of the pdBlocked matrix corresponding to pdIdent(~Plot-1) same thing for Block 2: VarietyL23, VarietyL54, VarietyL59, VarietyL71, VarietyL49, ... Formula: ~Variety - 1 | const Structure: Multiple of an Identity VarietyL23 VarietyL54 VarietyL59 VarietyL71 VarietyL49 VarietyL21 StdDev: 0.04296328 0.04296328 0.04296328 0.04296328 0.04296328 0.04296328 ... and Block 3: vtL18/Early, vtL18/Late, vtL21/Early, vtL21/Late, vtL23/Early, ... vtL71/Late Formula: ~vt - 1 | const Structure: Multiple of an Identity vtL18/Early vtL18/Late vtL21/Early vtL21/Late vtL23/Early vtL23/Late StdDev: 0.006307627 0.006307627 0.006307627 0.006307627 0.006307627 0.006307627 ... and Residual StdDev: 0.1299986 is just the sigma(e) Fixed effects: Measurement ~ Time Value Std.Error DF t-value p-value (Intercept) 9.762629 0.10228970 190 95.44097 <.0001 TimeLate -0.222656 0.03712913 190 -5.99680 <.0001 I'm still wondering why I only get TimeLate...is it because TimeEarly is just 0.222656? when I do: > intervals(Data.lme) Approximate 95% confidence intervals Fixed effects: lower est. upper (Intercept) 9.5608597 9.7626290 9.9643984 TimeLate -0.2958942 -0.2226559 -0.1494177 attr(,"label") [1] "Fixed effects:" Random Effects: Level: const lower est. upper sd(Plot - 1) 8.436375e-01 0.973655068 1.1237103 sd(Variety - 1) 1.577451e-02 0.042963279 0.1170143 sd(vt - 1) 4.186565e-06 0.006307627 9.5032935 Within-group standard error: lower est. upper 0.1119908 0.1299986 0.1509019 These are my confidence intervals on the variance components, I assume. I'm slightly confused on this because in one of the posts I found: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/21857.html it seems to be much more complicated to find the variance componenets. Thanks much, Scott Rifkin [EMAIL PROTECTED] ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html