Hi Alex,
Attachments aren't put through to the mailing list, you need to put them
on a publicly available share.
But thanks for putting forth a (soon-to-be) reproducible example!
Best,
Phillip
On Wed, 2016-05-04 at 10:49 +0200, Xandre wrote:
Thanks for the interest,
Just to check problems of code I tried again with a much more simple
example. I made a subset of my original data base (see attached .csv)
and run a much more simple model as follows:
*> M1<-glm(response~explanatory, **
**+ data=datos,**
**+ family="binomial")**
**> M2<-glmmadmb(response~explanatory, **
**+ data=datos,**
**+ family="binomial")**
**> **
**> summary(M1)*
Call:
glm(formula = response ~ explanatory, family = "binomial", data = datos)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.272 -1.226 1.089 1.128 1.549
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.537e-01 6.638e-02 3.822 0.000132 ***
explanatory -1.660e-04 5.214e-05 -3.183 0.001456 **
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3461.2 on 2499 degrees of freedom
Residual deviance: 3450.9 on 2498 degrees of freedom
AIC: 3454.9
Number of Fisher Scoring iterations: 3
*> summary(M2)*
Call:
glmmadmb(formula = response ~ explanatory, data = datos, family =
"binomial")
AIC: 3454.9
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.54e-01 6.64e-02 3.82 0.00013 ***
explanatory -1.66e-04 5.21e-05 -3.18 0.00146 **
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Number of observations: total=2500
Log-likelihood: -1725.45
*> newdatos <-
data.frame(explanatory=seq(min(datos$explanatory),max(datos$explanatory),length.out=10))*
*> pred1<-predict(M1,newdatos,type="link",se.fit =T)**
**> pred2<-predict(M2,newdatos,type="link",se.fit =T)*
*> cbind(pred1$fit,pred2$fit)*
[,1] [,2]
1 0.22051737 0.22051798
2 0.09972924 0.09972896
3 -0.02105888 -0.02106007
4 -0.14184701 -0.14184909
5 -0.26263513 -0.26263811
6 -0.38342326 -0.38342713
7 -0.50421139 -0.50421615
8 -0.62499951 -0.62500518
9 -0.74578764 -0.74579420
10 -0.86657576 -0.86658322
*> cbind(pred1$se.fit,pred2$se.fit)*
[,1] [,2]
1 0.05841106 0.06724456
2 0.04037125 0.08232769
3 0.05222381 0.10914997
4 0.08187989 0.14117163
5 0.11645089 0.17557048
6 0.15263291 0.21118808
7 0.18950541 0.24749882
8 0.22673178 0.28423718
9 0.26416245 0.32125649
10 0.30172140 0.35846971
#Although now de differences are lower, I think they still are quite
important.
This is my *sessionInfo()*:
R version 3.2.4 Revised (2016-03-16 r70336)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 Service Pack 1
locale:
[1] LC_COLLATE=Spanish_Spain.1252 LC_CTYPE=Spanish_Spain.1252
LC_MONETARY=Spanish_Spain.1252
[4] LC_NUMERIC=C LC_TIME=Spanish_Spain.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] glmmADMB_0.8.3.3 MASS_7.3-45
loaded via a namespace (and not attached):
[1] Matrix_1.2-4 plyr_1.8.3 magrittr_1.5 tools_3.2.4
coda_0.18-1 Rcpp_0.12.4 stringi_1.0-1
[8] nlme_3.1-126 grid_3.2.4 stringr_1.0.0 R2admb_0.7.13
lattice_0.20-33
Hopefully all this info will be helpful.
Thanks in advance for your time.
Regards,
Alex
El 04/05/2016 a las 0:28, Ben Bolker escribi?:
Not sure, this will be worth looking into ...
On 16-05-03 04:51 PM, Xandre wrote:
Dear list,
I am running a GLM (family="binomial") without random effects using both
glm and glmmadmb.
Summaries are almost identical, however when I used the predict function
as follows:
predict(glm1,newdatos1,type="link",se.fit =T)
predict(admb1,newdatos1,type="link",se.fit =T)
I realized that se.fit differ a lot between them, admb se.fit resulted
much much higher (fit is almost identical). This is just and example of
what I found:
glm1$se.fit admb1$se.fit
0.04290869 0.2676562
0.04435600 0.2733130
0.04095631 0.2728592
0.03402992 0.2718389
0.03000669 0.2713617
0.03633637 0.2722059
Maybe I'm missing something or I am making a big mistake. Any help with
this?
Many thanks,
Alexandre Alonso
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