package lme4

Thanks,Douglas,
It really helps me a lot, but?is there any other way?if I want to show
whether a random effect is significant in text file, like P value or other
?index.
Thanks very much again.
Wenjun.

2009/11/2 Douglas Bates <bates at stat.wisc.edu>

On Sun, Nov 1, 2009 at 9:01 AM, wenjun zheng <wjzheng09 at gmail.com> wrote:

Hi R Users,
? ? When I use package lme4 for mixed model analysis, I can't
distinguish
the significant and insignificant variables from all random independent
variables.
? ? Here is my data and result:
Data:


?Rice<-data.frame(Yield=c(8,7,4,9,7,6,9,8,8,8,7,5,9,9,5,7,7,8,8,8,4,8,6,4,8,8,9),
? ? ? ? ? ? ? ? Variety=rep(rep(c("A1","A2","A3"),each=3),3),
? ? ? ? ? ? ? ? Stand=rep(c("B1","B2","B3"),9),
? ? ? ? ? ? ? ? Block=rep(1:3,each=9))
? ?Rice.lmer<-lmer(Yield ~ (1|Variety) + (1|Stand) + (1|Block) +
(1|Variety:Stand), data = Rice)

Result:

Linear mixed model fit by REML
Formula: Yield ~ (1 | Variety) + (1 | Stand) + (1 | Block) + (1 |
Variety:Stand)
? Data: Rice
? AIC ? BIC logLik deviance REMLdev
?96.25 104.0 -42.12 ? ?85.33 ? 84.25
Random effects:
?Groups ? ? ? ?Name ? ? ? ?Variance Std.Dev.
?Variety:Stand (Intercept) 1.345679 1.16003
?Block ? ? ? ? (Intercept) 0.000000 0.00000
?Stand ? ? ? ? (Intercept) 0.888889 0.94281
?Variety ? ? ? (Intercept) 0.024691 0.15714
?Residual ? ? ? ? ? ? ? ? ?0.666667 0.81650
Number of obs: 27, groups: Variety:Stand, 9; Block, 3; Stand, 3;
Variety, 3

Fixed effects:
? ? ? ? ? ?Estimate Std. Error t value
(Intercept) ? 7.1852 ? ? 0.6919 ? 10.38

Can you give me some advice for recognizing the significant variables
among
random effects above without other ?calculating.

Well, since the estimate of the variance due to Block is zero, that's
probably not one of the significant random effects.

Why do you want to do this without other calculations? ?In olden days
when each model fit involved substantial calculations by hand one did
try to avoid fitting multiple models but now that is not a problem.
You can get a hint of which random effects will be significant by
looking at their precision in a "caterpillar plot" and then fit the
reduced model and use anova to compare models. ?See the enclosed

? ?Any suggestions will be appreciated.
Wenjun

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