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
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