Questions concerning glmmADMB and hurdle model

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
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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