[R-sig-ME] p-value absense in output lmer

[David Winsemius]

On Dec 16, 2011, at 5:49 PM, Søren Højsgaard wrote:

> The pbkrtest package provides such tests; either based on the  
> Kenward-Rogher approximation on parametric bootstrap methods.
> Regards
> Søren
>
> ________________________________________
> Fra: r-sig-mixed-models-bounces at r-project.org [r-sig-mixed-models-bounces at r-project.org 
> ] På vegne af Karina Villegas [villegaskary at gmail.com]
> Sendt: 16. december 2011 21:23
> Til: R-SIG-Mixed-Models at r-project.org
> Emne: [R-sig-ME] p-value absense in output lmer
>
> Dear R-mixed-model-experts:
>
> I am running R version 2.12.1 on Windows 2007. I am studying  
> environmental
> factors and maternal behavior in the body condition of sea lion pups  
> from
> California.
>
> I specified my model as follows:
>
> Model <- lmer
> (Condicion~DuracionNurse+FrecNurse+FrecInteraccion+Temperatura 
> +Densidad+(1|Mes)+(1|Zona)+(1|Sexo)+(1|Marea)+(1|Temporada),
> family=gaussian, data=Datos)
>
> (See output below)
>
> At this point, my main questions are:
>
> 1. Is my model correctly built?
>
> 2. Why don’t I get p values for t?
>
> 3. Is there any way to compute p values for the fixed effects?
>
>
> Linear mixed model fit by REML
> Formula: Condicion ~ DuracionNurse + FrecNurse + FrecInteraccion +
> Temperatura +      Densidad + (1 | Mes) + (1 | Zona) + (1 | Sexo) + (1
> | Marea) +      (1 | Temporada)
>   Data: Datos
>   AIC   BIC logLik deviance REMLdev
> 646.6 682.5 -311.3    619.1   622.6
> Random effects:
> Groups    Name        Variance   Std.Dev.
> Mes       (Intercept) 2.8159e-01 5.3065e-01
> Marea     (Intercept) 2.1107e-01 4.5943e-01
> Zona      (Intercept) 2.1056e-01 4.5886e-01
> Temporada (Intercept) 1.4041e-01 3.7472e-01
> Sexo      (Intercept) 6.9748e-11 8.3515e-06
> Residual              3.8768e+00 1.9690e+00
> Number of obs: 147, groups: Mes, 4; Marea, 3; Zona, 3; Temporada, 2;  
> Sexo, 2

Caveat: I'm not an expert in either your area or in mixed models, so  
I'm really just joining you in asking questions of the experts here.  
Q1: Does the fact that the product of the number of potential  
groupings on random effects = 4*3*3*2*2 == 144 is on the same order of  
the number of observations raise any concerns?
>
>
> Fixed effects:
>                 Estimate Std. Error t value
> (Intercept)      69.01515   17.74172   3.890
> DuracionNurse     0.04564    0.27483   0.166
> FrecNurse         3.00491    0.79289   3.790
> FrecInteraccion  -0.62826    0.17585  -3.573
> Temperatura      -2.87229    0.91138  -3.152
> Densidad        -18.78045    6.22638  -3.016
>
> Correlation of Fixed Effects:
(DW: Edited the correlation matrix)

>              (Intr) DrcnNr FrcNrs FrcInt Tmprtr
> DuracionNrs   0.741
> FrecNurse     0.945 0.582
> FrecIntrccn  -0.965 -0.691 -0.977
> Temperatura  -0.999 -0.741 -0.954 0.972
> Densidad     -0.919 -0.878 -0.823 0.862 0.915

Q2: Does the fact that many of those numbers are above 0.95 worry  
anybody else? I would not have expected such high correlations in  
"real biological data".

-- 
David Winsemius, MD
West Hartford, CT