[R-sig-ME] Why se.fit differ in predict.glm and predict.glmmadmb?

[Xandre]

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 escribió:
>    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
>>
>>
>> 	[[alternative HTML version deleted]]
>>
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