Hi Emma,

I am a newcomer to linear mixed effects models, and so my answer to you
question should be confirmed with others who have more experience. My guess
is that your random effect, Year, is not accounting for very much
variability in the outcome, RW. This would lead to the large Std Dev for the
random effect of Year and the point estimate being unreliable.

Does this make sense in the context of your data?

-Cyrus


On Thu, Aug 11, 2011 at 10:36 AM, Emma Jones <stp08emj@sheffield.ac.uk>wrote:

>
>
> Hi,
>
> I am new to using the package lme4 and wondered if anyone can help me? I
> have fitted a model (note the data is unbalanced)- see below with the
> results:
>
> >/  a=lmer(RW~1+(1|Year),chron,na.action=na.omit)
> />/  a
> /Linear mixed model fit by REML
> Formula: RW ~ 1 + (1 | Year)
>    Data: chron
>   AIC  BIC logLik deviance REMLdev
>  8771 8790  -4383     8761    8765
> Random effects:
>  Groups   Name        Variance Std.Dev.
>  Year     (Intercept) 0.48050  0.69318
>  Residual             0.52704  0.72598
> Number of obs: 3719, groups: Year, 239
>
> Fixed effects:
>             Estimate Std. Error t value
> (Intercept) -0.02793    0.04803 -0.5815
>



-- 
http://www.ualberta.ca/~cshaoul/

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