I'm facing a problem with a binomial glmer
model. I want to find if differences in flower presence in pine trees is due to procedence of the tree.
My model is as follows: FlorMas ~ Proc + (1|Blq)
.
Proc is a factor with nine levels, one of it (TAMR
) presents no flower at all (variable value for all TAMR
trees is 0).
This model gives me this output:
Generalized linear mixed model fit by the Laplace approximation
Formula: FlorMas ~ Proc + (1 | Blq)
Data: flower.data
AIC BIC logLik deviance
593 647.7 -285.5 571
Random effects:
Groups Name Variance Std.Dev.
Blq (Intercept) 0.18476 0.42983
Number of obs: 1067, groups: Blq, 8
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.7668 0.2958 -5.974 2.32e-09 ***
ProcTAMR -16.8758 1080.5608 -0.016 0.98754
ProcARMY -0.3543 0.3910 -0.906 0.36490
ProcASPE -1.4891 0.5260 -2.831 0.00464 **
ProcCOCA -2.4947 0.7619 -3.274 0.00106 **
ProcMIMI -1.2040 0.4930 -2.442 0.01459 *
ProcORIA -1.5360 0.5739 -2.676 0.00744 **
ProcPLEU -1.9437 1.0538 -1.845 0.06511 .
ProcPTOV 0.1693 0.3508 0.483 0.62945
ProcSCRI 0.5060 0.3346 1.512 0.13050
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
I don't understand that values for TAMR
procedence, as if it has all zero values it should be different from the others.
Any help will be appreciated.