Dear Douglas and list members,

Apologies in advance if you might consider my questions as too simple to
be asking the godfather of lme4 for an answer...thus, please feel free to
ignore my email or to forward it to someone else.

I'm a PhD student (Australia/Germany) working on tropical seabirds. As
many of my PhD-collegues, I'm having some difficulties with the analysis
of my data using lmer (family=binomial). While some say: What do you care

about all the other values as long as you've got a p-value... I do believe
that it is essential to understand WHAT I'm doing here and WHAT all these
numbers/values mean.

I've read the Chapters (lme4 Book Chapters) and publications about the use

of lmer and searched the forums - but I don't find a satisfying answer.
And I have the feeling that 1. the statistic lecture at my university was
a joke (sad to say this) 2. that I need a huge statistical/mathematical

background to fully understand GLMMs.


One of the question I would like to answer is:
Does the previous breeding success influences nest site fidelity?

I have binomial data:
SameSite=1 means birds use the same site

SameSite=0 means birds change nest site

BreedSuc1=1 Birds were successful in previous breeding season
BreedSuc1=0 Birds were not successful "     "        "

Sex= male, female
Bird= Bird ID

This is my model:
fm<-lmer(SameSite~BreedSuc1+Sex+(1|Bird), family="binomial")

where Bird is my random factor (same birds were sampled more than once)

summary(fm)

Generalized linear mixed model fit by the Laplace approximation

Formula: SameSite ~ BreedSuc1 + Sex + (1 | Bird)
   AIC   BIC logLik deviance
 77.38 85.34 -34.69    69.38
Random effects:
 Groups Name        Variance Std.Dev.
 Bird   (Intercept) 0.14080  0.37524
Number of obs: 54, groups: Bird, 46

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  -0.3294     0.4890  -0.674   0.5006
BreedSuc11    1.1988     0.5957   2.012   0.0442 *
SexM          0.2215     0.5877   0.377   0.7062

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
           (Intr) BrdS11
BreedSuc11 -0.536
SexM       -0.628  0.065


>From this summary output I do understand that the Breeding Success has a

significant effect on nest-site fidelity (p<0.05).

But what else can I conclude from this model?

Questions:

1.Random effects: What does the Random Effect table - the Variance, Std.
Dev. and Intercept - tells me: Is there a random effect that my model has

to account for?

Random effects:
 Groups Name        Variance Std.Dev.
 Bird   (Intercept) 0.14080  0.37524
Number of obs: 54, groups: Bird, 46

2. Fixed Effects: Again the Intercept? Not sure if I understand the

meaning of it (sorry, explanation in Chapter I also doesn't help much)

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  -0.3294     0.4890  -0.674   0.5006
BreedSuc11    1.1988     0.5957   2.012   0.0442 *

SexM          0.2215     0.5877   0.377   0.7062

3. Meaning of the z-value? Why shall I mention it in te result section?

4. Estimate and Std. Error of the fixed effects? How can I tell from these
values WHAT kind of effect (positiv, negativ?) these parameter have on

nest-site fidelity? Do birds that were successful during the previous
breeding success show a higher nest-site fidelity? Remember, I have
binomial data...

I would highly appreciate your feedback and/or suggestions of

papers/chapters I could read for a better understanding of the output.

Best regards,


Julia

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