[R-sig-ME] random factor variance
João R.
joaordaniel at gmail.com
Wed May 20 14:07:20 CEST 2009
Andy,
here goes the model with no fixed effects. (I've attached a plot)
Generalized linear mixed model fit by the Laplace approximation
Formula: DV ~ (1 | R1)
Data: JD
AIC BIC logLik deviance
223.1 229.6 -109.5 219.1
Random effects:
Groups Name Variance Std.Dev.
R1 (Intercept) 0.17255 0.41539
Number of obs: 190, groups: VICT2, 14
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.9106 0.2149 -4.237 2.27e-05 ***
Thanks
On Wed, May 20, 2009 at 12:53 PM, Andy Fugard <andy.fugard at sbg.ac.at> wrote:
> What happens if you fit the model with no fixed effects? Is there then
> variances for R1?
>
> Also can you plot the data in some way, e.g.,
>
> histogram(~ DV | R1, data = ...)
>
> A
>
>
>
> João R. wrote:
>
>> Thanks Ken, but I did not fully understood you.
>>
>> *This means that the variance of the random effect needed to explain your
>> data is zero.*
>> This part I get, although the fact that the value for variance is an
>> absolute 0 makes me wonder if there is something wrong. I would be happy
>> with a low value, but not exactly 0.
>> The fact that two of the fixed factors are continuous variables might have
>> something to do with it?
>> *The clusters vary by the same amount or less than if there was a random
>> effect, that is they can all be explained by subject variation.*
>> This part I don't follow...
>>
>> Basically, I am trying to predict the occurrence of reconciliation after
>> conflicts in a primate group (dependent variable: 0-no occurrence;
>> 1-reconciliation). My random variable is the victim's identity of these
>> conflicts (since not all group members are victims of conflicts, and some
>> are "more victims" than others). As fixed effects I have a set of
>> variables
>> (describing the type of conflict and the relationship between opponents;
>> 23
>> variables), some continuous (ex. F1, F3) and other categorical (ex. F2, F4
>> e
>> F5). Using a forward selection procedure based on AIC values, the best fit
>> model is this one I presented with the five fixed factors.
>>
>> Thanks again.
>>
>>
>>
>>
>> On Wed, May 20, 2009 at 10:09 AM, Ken Beath <ken at kjbeath.com.au> wrote:
>>
>> On 20/05/2009, at 11:43 AM, João R. wrote:
>>>
>>> Hello,
>>>
>>>> I have recently used lme4 package to run a glmm, but a get 0 variance
>>>> explained by the random effect. The model has 5 fixed effects, and I
>>>> have
>>>> run each of them separately and for two of them (F1, F3) I also get 0
>>>> variance for the random effect. Do you have any ideas of what might be
>>>> causing this? Is this kind of result to be expected?
>>>> thanks
>>>>
>>>>
>>>> This means that the variance of the random effect needed to explain your
>>> data is zero. The clusters vary by the same amount or less than if there
>>> was a random effect, that is they can all be explained by subject
>>> variation.
>>>
>>> Ken
>>>
>>>
>>>
>>> Generalized linear mixed model fit by the Laplace approximation
>>>> Formula:
>>>>
>>> ~ F1 + F2 + F3 + F4 + F5 + (1 | R1)
>
>> Data: JD
>>>> AIC BIC logLik deviance
>>>> 203.2 225.9 -94.6 189.2
>>>> Random effects:
>>>> Groups Name Variance Std.Dev.
>>>> R1 (Intercept) 0 0
>>>> Number of obs: 190, groups: R1, 14
>>>> Fixed effects:
>>>> Estimate Std. Error z value Pr(>|z|)
>>>> (Intercept) 1.8949 1.1869 1.596 0.11039
>>>> F1 4.6740 2.4365 1.918 0.05507 .
>>>> F2 -2.0657 0.7543 -2.739 0.00617 **
>>>> F3 21.8036 8.8890 2.453 0.01417 *
>>>> F4 1.0968 0.4874 2.250 0.02444 *
>>>> F5 -1.7430 0.9583 -1.819 0.06894 .
>>>>
>>>> [[alternative HTML version deleted]]
>>>>
>>>> _______________________________________________
>>>> R-sig-mixed-models at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>
>>>>
>>>>
>> [[alternative HTML version deleted]]
>>
>>
>>
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>>
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>
>
> --
> Andy Fugard, Post-doc, ESF LogICCC (LcpR) project
> Fachbereich Psychologie, Universitaet Salzburg
> Hellbrunnerstr. 34, 5020 Salzburg, Austria
> +43 (0)680 2199 346 http://figuraleffect.googlepages.com
>
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