On 18/12/11 05:52, David Winsemius wrote:
> 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@r-project.org [r-sig-mixed-models-bounces@r-project.org
>> ] P&#229; vegne af Karina Villegas [villegaskary@gmail.com]
>> Sendt: 16. december 2011 21:23
>> Til: R-SIG-Mixed-Models@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?

I don't know! You will need to examine some diagnostics. Do you think 
interaction terms may be important? There are no interaction terms in 
your model. Is the random effects structure appropriate? Does the model 
reflect your sampling design appropriately?
>>
>> 2. Why don’t I get p values for t?
This is a FAQ. See the R FAQ.


      7.35 Why are p-values not displayed when using lmer()?

Doug Bates has kindly provided an extensive response in a post to the 
r-help list, which can be reviewed at 
https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html.


>>
>> 3. Is there any way to compute p values for the fixed effects?
Yes, if you really must. See the package languageR, functions pvals.fnc 
and aovlmer.fnc. Essentially, you have to give up on estimating degrees 
of freedom for F and t tests, and cut the Gordion knot by using MCMC and 
Bayesian inference.


>>
>>
>> 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?
Yes! It's going to be hard to estimate variances of random effects when 
their are only 2-4 levels. My usual rule of thumb is 6 or more.

>>
>> 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".
>
Not really. If the data are not centred, then you expect big 
correlations between the slopes and intercepts. It can help to centre 
the data for numerical reasons, but it shouldn't affect the inferences. 
Or am I wrong? Please correct me, someone!

Cheers,

Simon.

-- 
Simon Blomberg, BSc (Hons), PhD, MAppStat, AStat.
Lecturer and Consultant Statistician
School of Biological Sciences
The University of Queensland
St. Lucia Queensland 4072
Australia
T: +61 7 3365 2506
email: S.Blomberg1_at_uq.edu.au
http://www.uq.edu.au/~uqsblomb/

Policies:
1.  I will NOT analyse your data for you.
2.  Your deadline is your problem

Statistics is the grammar of science - Karl Pearson.


	[[alternative HTML version deleted]]


