[R-sig-ME] lmer and p-values

Ben Bolker bbolker at gmail.com
Mon Mar 28 23:14:34 CEST 2011

```On 03/28/2011 01:05 PM, Iker Vaquero Alba wrote:
>
>    Also, how would it be possible to guess an appropriate denominator df?
>
>    Thanks
>

For example, consider the denominator dfs expected from the classical
literature with each set of blocks that you are using, considered
separately (e.g. for 10 spatial blocks and 19 temporal blocks, compute
the classical expectation for spatial blocking only and temporal
blocking only).  Use the smaller of these df as a conservative estimate.

> --- El *lun, 28/3/11, Ben Bolker /<bbolker at gmail.com>/* escribió:
>
>
>     De: Ben Bolker <bbolker at gmail.com>
>     Asunto: Re: [R-sig-ME] lmer and p-values
>     Para: r-sig-mixed-models at r-project.org
>     Fecha: lunes, 28 de marzo, 2011 18:27
>
>     Iker Vaquero Alba <karraspito at ...> 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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>

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