[R-sig-ME] Questions concerning glmmADMB and hurdle model
Amirouche Sadoun
amirouche.sadoun at cerco.ups-tlse.fr
Wed Mar 30 11:46:35 CEST 2016
Dear Sir,
I'm currently a PhD student in cognitive neurosciences at the CerCo
laboratory, France.
I'm dealing with a problematic trying to analyze my data with the
glmmADMB function in R.
I thank you in advance for paying attention to my present problem
regarding the use of a Hurdle model with glmmADMB, because I didn't yet
found a solution, especially as I am new in using R.
I'm trying to analyze behavioral data containing a given number of
trials called ab. (Experimental design with repeated measures. Fixed
effects: GROUP and DELAY; random effect: the id of the subjects).
(Please find the data attached).
The response variable is the number of AB responses related to the Total
number of trials.
In this data, many subjects had 0 values . This requires the use of a
Zero inflated or Hurdle model with glmmADMB function (), as it was
recommended to me.
I tried than to do the analysis with this function, even if it seems not
clear to me, but I get error messages, in addition to that I don't know
how to deal with both models after (with post hoc analysis).
Please, find the following lines of the R code and the error messages:
datab $ ID <-factor ($ datab ID)
mod1 <-glmmadmb (cbind (AB, Total-AB) ~ * DELAY GROUP + (1 | ID), data =
subset (datab, AB> 0), family = "truncnbinom1")
datab $ nz <- as.numeric (datab $ AB> 0)
mod2 <-glmmadmb (nz ~ * DELAY GROUP + (1 | ID), data = datab, family =
"binomial")
The error message: [1] NOTICE: Warning in eval (expr, envir, pen):
sd.est not defined for this family
[2] ERROR:
The maximizer function failed (could not find parameter file)
Troubleshooting steps include (1) run with
'Save.dir' set and inspect output files; (2) changes run parameters
I hope wholeheartedly find help
Please accept the assurance of my distinguished regards.
A. SADOUN
CerCo, UMR 5549
Pavillon Baudot
Toulouse 31052 FRANCE
-------------- next part --------------
ID DELAY GROUP Total AB
1 ph1 K 506 0
2 ph1 K 902 6
3 ph1 K 1503 0
4 ph1 K 1958 8
5 ph1 K 806 7
6 ph1 K 770 0
7 ph1 K 502 2
8 ph1 K 1398 0
9 ph1 K 1874 0
10 ph1 K 1432 6
11 ph1 K 689 5
12 ph1 K 1670 2
13 ph1 K 1860 13
14 ph1 W 2314 82
15 ph1 W 2903 33
16 ph1 W 1034 17
17 ph1 W 705 4
18 ph1 W 4305 75
19 ph1 W 1684 66
20 ph1 W 1188 19
21 ph1 W 880 11
22 ph1 W 304 3
23 ph1 W 2108 41
24 ph1 W 2486 132
25 ph1 W 2581 69
1 ph2 K 690 5
2 ph2 K 683 0
3 ph2 K 802 3
4 ph2 K 705 2
5 ph2 K 758 5
6 ph2 K 769 0
7 ph2 K 604 2
8 ph2 K 860 1
9 ph2 K 801 0
10 ph2 K 702 0
11 ph2 K 764 0
12 ph2 K 870 0
13 ph2 K 588 2
14 ph2 W 803 0
15 ph2 W 550 3
16 ph2 W 689 3
17 ph2 W 716 3
18 ph2 W 549 10
19 ph2 W 766 0
20 ph2 W 654 15
21 ph2 W 703 61
22 ph2 W 590 0
23 ph2 W 641 15
24 ph2 W 784 33
25 ph2 W 699 4
1 ph3 K 201 0
2 ph3 K 198 0
3 ph3 K 168 0
4 ph3 K 197 1
5 ph3 K 198 0
6 ph3 K 196 0
7 ph3 K 203 0
8 ph3 K 187 3
9 ph3 K 193 0
10 ph3 K 195 0
11 ph3 K 201 1
12 ph3 K 195 0
13 ph3 K 178 0
14 ph3 W 199 0
15 ph3 W 205 5
16 ph3 W 203 0
17 ph3 W 199 0
18 ph3 W 192 12
19 ph3 W 201 7
20 ph3 W 189 0
21 ph3 W 203 0
22 ph3 W 210 3
23 ph3 W 204 3
24 ph3 W 197 8
25 ph3 W 181 5
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