May be I can calculate p value by t testing approximately:
1-qnorm(Variance/Std.Dev.)
But which function can help me to extract Variance and Std.Dev values from
the results below:
>print(fm2 <- lmer(Yield ~ 1 + (1|Stand) + (1|Variety) +
(1|Variety:Stand),Rice))
Linear mixed model fit by REML
Formula: Yield ~ 1 + (1 | Stand) + (1 | Variety) + (1 | Variety:Stand)
Data: Rice
AIC BIC logLik deviance REMLdev
94.25 100.7 -42.12 85.33 84.25
Random effects:
Groups Name Variance Std.Dev.
Variety:Stand (Intercept) 1.345679 1.16003
Variety (Intercept) 0.024692 0.15714
Stand (Intercept) 0.888888 0.94281
Residual 0.666667 0.81650
Number of obs: 27, groups: Variety:Stand, 9; Variety, 3; Stand, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.1852 0.6919 10.38
2009/11/2 Douglas Bates <ba...@stat.wisc.edu>
> On Sun, Nov 1, 2009 at 9:01 AM, wenjun zheng <wjzhen...@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
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
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