Tengo el siguiente modelo:
> model1<-lmer(aph.remain~sMFS1+sAG1+sSHDI1+sbare+season+crop
+(1|landscape),family=poisson)
.. .y este es el Resumen de la salida.
> summary(model1)
Generalized linear mixed model fit by the Laplace approximation
Formula: aph.remain ~ sMFS1 + sAG1 + sSHDI1 + sbare + season + crop
+ (1 | landscape)
AIC BIC logLik deviance
4057 4088 -2019 4039
Random effects:
Groups Name Variance Std.Dev.
landscape (Intercept) 0.74976 0.86588
Number of obs: 239, groups: landscape, 45
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.6613761 0.1344630 19.793 < 2e-16
sMFS1 0.3085978 0.1788322 1.726 0.08441
sAG1 0.0003141 0.1677138 0.002 0.99851
sSHDI1 0.4641420 0.1619018 2.867 0.00415
sbare 0.4133425 0.0297325 13.902 < 2e-16
seasonlate -0.5017022 0.0272817 -18.390 < 2e-16
cropforage 0.7897194 0.0672069 11.751 < 2e-16
cropsoy 0.7661506 0.0491494 15.588 < 2e-16
Correlation of Fixed Effects:
(Intr) sMFS1 sAG1 sSHDI1 sbare sesnlt crpfrg
sMFS1 -0.007
sAG1 0.002 -0.631
sSHDI1 0.000 0.593 -0.405
sbare -0.118 -0.003 0.007 -0.013
seasonlate -0.036 0.006 -0.006 0.003 -0.283
cropforage -0.168 -0.004 0.016 -0.014 0.791 -0.231
cropsoy -0.182 -0.028 0.030 -0.001 0.404 -0.164 0.557
Es probablemente overdispersed, pero ¿cómo se calcula esto?
Muchas gracias.
