
   Thank you very much. My data set consists of about 80 to 90 data points. 

   If I still have to use mcmcsamp(), could anyone recommend me where I can find any information about its usage?

   Thenk you for your help.

--- El lun, 28/3/11, Ben Bolker <bbolker@gmail.com> escribió:

De: Ben Bolker <bbolker@gmail.com>
Asunto: Re: [R-sig-ME] lmer and p-values
Para: r-sig-mixed-models@r-project.org
Fecha: lunes, 28 de marzo, 2011 18:27

Iker Vaquero Alba <karraspito@...> writes:

> 
> 
>    Dear list members:
> 
>    I am fitting a model with lmer, because I need to fit some nested
> as well as non-nested random effects in it. I am doing a split plot
> simplification, dropping terms from the model and comparing the models with or
> without the term. When doing and ANOVA between one model and its simplified
> version, I get, as a result, a chisquare value with 1 df (df from the bigger
> model - df from the simplified one), and a p-value associated.
> 
>    I was just wondering if it's correct to present this chisquare and
> p values as a result of testing the effect of a certain term in the model. I am
> a bit confused, as if I was doing this same analysis with lme, I would be
> getting F-values and associated p-values.
> 

  When you do anova() in this context you are doing a likelihood ratio
test, which is equivalent to doing an F test with 1 numerator df and
a very large (infinite) denominator df.  
  As Pinheiro and Bates 2000 point out, this is dangerous/anticonservative
if your data set is small, for some value of "small".
   Guessing an appropriate denominator df, or using mcmcsamp(), or parametric
bootstrapping, or something, will be necessary if you want a more
reliable p-value.

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