random factor variance
On 20/05/2009, at 9:35 PM, 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.
Because the value can't go below zero, it may end up as zero. Even if the true value is small but non-zero then sampling variation may cause the estimate to be zero.
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...
Think about a population and then dividing it randomly into a number of groups. I will assume normal distributions but the same ideas apply to binomial etc. We would expect that the groups will have different means, and we know how they will vary based on the population variance. When we fit a random effect the question we are asking is do these vary more than predicted from the population, in which case our random effect variance will be greater than zero.
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.
I suspect the problem is that your model is overfitted, because of the number of possible covariates, and the stepwise selection it has constructed a model that fits well without need for a random effect. Ken
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: DV ~ 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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