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 Jo?o 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, Jo?o 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 .
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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