[R-sig-ME] Zero variance and Std. Dev. using lmer?

[ONKELINX, Thierry]

Dear Luciano,

Your variables are strongly correlated. Sibling competition is only
absent when the clutch size is one. Likewise the hatching order can only
be three if the clutch size is three. This could cause numberical
instability of your model. So I suggest that you simplify your model.
What results do you get with this model: lmer(Death10 ~ ClutchSize + Yr
+ (1|NestID), family = binomial)

HTH,

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium 
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 
www.inbo.be 

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] Namens Luciano La Sala
Verzonden: dinsdag 13 januari 2009 15:16
Aan: r-sig-mixed-models at r-project.org
Onderwerp: [R-sig-ME] Zero variance and Std. Dev. using lmer?

Dear R-people: 



I have run a GLMM (using lmer) with the fixed and random effects
detailed
below. Oddly I think, I get zero variance and Std. Dev. values. 

How is that possible? Does it mean that the RE "NestID" is not helping
to
account for autocorrelation among sibling chicks at the nest level? 

Or is this a small sample size problem? 



As well, I ran an ordinary logistic regression using he exact same fixed
variables, and I got the exact same AIC and BIC values and estimates for
fixed effects, error, z value, and Pr(>|z|). 



Does this support the idea that the GLMM with RE for NestID is not
necessary
at all? 



Look forward to hearing from you. 

Cheers for now.



Luciano   





GENERALIZED LINEAR MIXED MODEL WITH RANDOM INTERCEPT



model <-
lmer(Death10~HO+ClutchSize+SibComp+Yr+(1|NestID),family=binomial,1)



Generalized linear mixed model fit by the Laplace approximation 

Formula: Death10 ~ HO + ClutchSize + Sibcomp + yr + (1 | NestID) 



Data: 1 

AIC      BIC       logLik     deviance

242.2    268.5     -113.1     226.2



Random effects:

Groups Name           Variance      Std. Dev.

NestID (Intercept)    0                  0      



Number of obs: 198, groups: NestID, 104



Fixed effects:

                                   Estimate                     Std.
Error
z value             Pr(>|z|)  

(Intercept)                   -1.2239                      0.5114
-2.3934           0.0167 *

HOSecond                  -0.6910                      0.8928
-0.7739           0.4390  

HOThird                       0.6768                       1.0327
0.6554             0.5122  

ClutchSizeTwo-eggs     1.3961                     0.5864
2.3809
0.0173 *

ClutchSizeThree-eggs   0.3958                      0.5843
0.6773
0.4982  

SibcompAbsent             1.7804                     0.9140
1.9479             0.0514 .

yr2007                         -0.8299                      0.3423
-2.4245           0.0153 *

---



Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 





Correlation of Fixed Effects:

                          (Intr)      HOScnd     HOThrd     CltchSzTw-
CltchSzTh-   SbcmpA

HOSecond        -0.034                                            

HOThird           -0.031    0.837                                    

CltchSzTw-g   -0.830   -0.069          -0.022


CltchSzThr-      -0.816   -0.088          -0.107         0.785


SibcmpAbsnt     0.052   -0.904          -0.836       -0.018
-0.050           

yr2007              -0.233    0.145           0.133         0.025
-0.050       - 0.224






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