Dear Collin
Try using lme (library(nlme)  to fit the Model and obtain the usual p-valúes

Model=lme(TN~wsh*rip, random=(~1|stream), data=all24)
anova(Model)

May be, you can also reduce your Model dropping the interaction term

El martes 22 de noviembre de 2011, Ben Bolker <bbolker@gmail.com> escribió:
> Colin Wahl <biowahl@...> writes:
>
>> I am using lmer to describe patterns in stream variables between
>> streams draining watersheds with different land uses. I need to make
>> some kind of determination of significance. I understand that mixed
>> models cannot provide accurate p values because the denominator df is
>> unknown. As per D. Bates recommendation in his post "lmer, p-values
>> and all that" I used mcmcsamp and HPDinterval to get 95% "confidence
>> intervals." These intervals were very large and suggest that there are
>> no significant differences in variables where differences seem
>> obvious. I'll use one of my variables as an example: total stream
>> nitrogen. There are 12 streams and 24 sites (two riparian types per
>> stream).
>>
>> model<-lmer(TN ~ wsh*rip + (1|stream), data=all24)
>>    Data: all24
>>    AIC   BIC logLik deviance REMLdev
>>  78.47 90.25 -29.24    65.15   58.47
>> Random effects:
>>  Groups   Name        Variance Std.Dev.
>>  stream   (Intercept) 4.10443  2.0259
>>  Residual             0.20885  0.4570
>> Number of obs: 24, groups: stream, 12
>
>  Sorry for all the snippage, but: since you have two riparian
> sites per stream, can you try something much simpler and reduce
> this to a series of paired t-tests and 1-way ANOVAs?
>
>  You say you have 24 sites, divided into 12 streams and
> two riparian sites per stream.  It sounds like streams are
> nested within watersheds, so you have (an average of) 3 streams
> per watershed/land use type (cultivated, D, F, G).
>
>  If you average the two sites per stream you can do a
> one-way ANOVA on the effect of land use type.
>
>  You can do a paired t-test on riparian types within streams.
>
>  If you take the differences within streams you can do a one-way
> ANOVA on the differences across land use types to test the
> interaction between land use type and riparian type.
>
>  Modern mixed models are great but sometimes there is a simpler
> and less error-prone way to do the job ...
>
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>

-- 
Prof. Julio Di Rienzo
Estadística y Biometría
FCA- U.N. Córdoba
IBS-RARG President
http://sites.google.com/site/juliodirienzo
"Biometry, the active pursuit of biological
knowledge by quantitative methods."
(R.A. Fisher, 1948)

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