Hello Folks,

Very new to R so bear with me, running 5.2 on XP.  Trying to do a zero-inflated negative binomial regression on placental scar data as dependent.  Lactation, location, number of tick larvae present and mass of mouse are independents.  Dataframe and attributes below:


 Location         Lac Scars Lar Mass Lacfac
1   Tullychurry   0     0  15 13.87      0
2      Somerset   0     0   0 15.60      0
3     Tollymore   0     0   3 16.43      0
4     Tollymore   0     0   0 16.55      0
5       Caledon   0     0   0 17.47      0
6  Hillsborough   1     5   0 18.18      1
7       Caledon   0     0   1 19.06      0
8   Portglenone   0     4   0 19.10      0
9   Portglenone   0     5   0 19.13      0
10    Tollymore   0     5   3 19.50      0
11 Hillsborough   1     5   0 19.58      1
12  Portglenone   0     4   0 19.76      0
13      Caledon   0     8   0 19.97      0
14 Hillsborough   1     4   0 20.02      1
15  Tullychurry   0     3   3 20.13      0
16 Hillsborough   1     5   0 20.18      1
17   LoughNavar   1     5   0 20.20      1
18    Tollymore   0     0   1 20.24      0
19 Hillsborough   1     5   0 20.48      1
20      Caledon   0     4   1 20.56      0
21      Caledon   0     3   2 20.58      0
22    Tollymore   0     4   3 20.58      0
23    Tollymore   0     0   2 20.88      0
24 Hillsborough   1     0   0 21.01      1
25  Portglenone   0     5   0 21.08      0
26  Tullychurry   0     2   5 21.28      0
27 Ballysallagh   1     4   0 21.59      1
28      Caledon   0     0   1 21.68      0
29 Hillsborough   1     5   0 22.09      1
30  Tullychurry   0     5   5 22.28      0
31  Tullychurry   1     6  75 22.43      1
32 Ballysallagh   1     5   0 22.57      1
33 Ballysallagh   1     4   0 22.67      1
34   LoughNavar   1     5   3 22.71      1
35 Hillsborough   1     4   0 23.01      1
36      Caledon   0     0   3 23.08      0
37   LoughNavar   1     5   0 23.53      1
38 Ballysallagh   1     4   0 23.55      1
39  Portglenone   1     6   0 23.61      1
40   Mt.Stewart   0     3   0 23.70      0
41     Somerset   0     5   0 23.83      0
42 Ballysallagh   1     5   0 23.93      1
43 Ballysallagh   1     5   0 24.01      1
44      Caledon   0     0   3 24.14      0
45   LoughNavar   0     6   0 24.30      0
46   LoughNavar   1     5   0 24.34      1
47 Hillsborough   1     4   0 24.45      1
48      Caledon   0     3   2 24.55      0
49  Tullychurry   0     5  44 24.83      0
50 Hillsborough   1     5   0 24.86      1
51 Ballysallagh   1     5   0 25.02      1
52  Tullychurry   0     0   9 25.27      0
53   Mt.Stewart   0     5   0 25.31      0
54   LoughNavar   1     4   8 25.43      1
55     Somerset   1     0   0 25.58      1
56 Hillsborough   1     5   0 25.82      1
57  Portglenone   1     2   0 26.02      1
58 Ballysallagh   1     5   0 26.19      1
59   Mt.Stewart   1     0   0 26.66      1
60  Randalstown   1     0   1 26.70      1
61     Somerset   0     4   0 27.01      0
62   Mt.Stewart   0     4   0 27.05      0
63     Somerset   0     3   0 27.10      0
64     Somerset   0     6   0 27.34      0
65     Somerset   0     0   0 27.87      0
66   LoughNavar   1     5   1 28.01      1
67  Tullychurry   1     6  42 28.55      1
68 Hillsborough   1     5   0 28.84      1
69  Portglenone   1     4   0 29.00      1
70     Somerset   1     4   0 31.87      1
71 Ballysallagh   1     5   0 33.06      1
72   LoughNavar   1     4   0 33.24      1
73     Somerset   1     4   0 33.36      1

alan : 'data.frame':    73 obs. of  6 variables:
 $ Location: Factor w/ 10 levels "Ballysallagh",..: 10 8 9 9 2 3 2 6 6 9 ...
 $ Lac     : int  0 0 0 0 0 1 0 0 0 0 ...
 $ Scars   : int  0 0 0 0 0 5 0 4 5 5 ...
 $ Lar     : int  15 0 3 0 0 0 1 0 0 3 ...
 $ Mass    : num  13.9 15.6 16.4 16.6 17.5 ...
 $ Lacfac  : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...

The syntax I used to create the model is:

zinb.zc <- zicounts(resp=Scars~.,x =~Location + Lar + Mass + Lar:Mass + Location:Mass,z =~Location + Lar + Mass + Lar:Mass + Location:Mass, data=alan)

The error given is:

Error in optim(par = parm, fn = neg.like, gr = neg.grad, hessian = TRUE,  : 
        non-finite value supplied by optim
In addition: Warning message:
fitted probabilities numerically 0 or 1 occurred in: glm.fit(zz, 1 - pmin(y, 1), family = binomial())

I understand this is a problem with the model I specified, could anyone help out??

Many thanks

Alan Harrison

Quercus
Queen's University Belfast
MBC, 97 Lisburn Road
Belfast

BT9 7BL

T: 02890 972219
M: 07798615682


	[[alternative HTML version deleted]]

