On Tue, Nov 3, 2009 at 9:11 AM, wenjun zheng wrote:
> May be I can calculate p value by t testing approximately:
> 1-qnorm(Variance/Std.Dev.)
That would be a z test, not a t test, wouldn't it? And it would only
be meaningful if the distribution of the estimator is approximately
normal, which we
On Tue, Nov 3, 2009 at 8:08 AM, wenjun zheng wrote:
> 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.
Well there are p-values from th
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
For
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
> On Sun, Nov 1, 2009 at 9:01 AM, wenjun zheng wrote:
> > Hi R Users,
On Sun, Nov 1, 2009 at 9:01 AM, wenjun zheng 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
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),
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