[ADMB Users] gmml.admb error

Fri Apr 1 05:24:44 PDT 2011

Hi,

I'm very new to R and gmml.admb so my apologies if my question is very basic.
 I'm looking at the relationships of climatic and methodological variables
on terrestrial carnivores track counts.

I have a number of fixed predictor variables and I don’t need to include any
random variable.  My response variable InTOT ( ie. the KAI (kilometric
index of abundance) of total carnivore species per 2x2 km grid) is overdispersed
so I'm trying to use glmm.admb for count  data with a negative binomial distribution. However,
it requires a grouping variable which confuses me because I supposed it should
be a random variable but in my case I don´t have anyone…..so what should I have
to include as “group” variable? Anyway I tried to include the variable OBS (the
observer who carried out the track count) as my grouping variable (i.e assuming
that it could acts as a random variable) but, when I wrote my command, I got
the following error message:

Error en glmm.admb(InTOT ~
VELMEDIA + TEMP + DIASULTLL + VVienM + (1 | 
:   The function maximizer failed (see below for total output)

Can
anyone suggest what I need to do or what is it happening?

This
is my model command

> model1 <-
glmm.admb(InTOT ~  VELMEDIA + TEMP +
DIASULTLL + VVienM  + (1 | BLOQUE),
data=CAR08, group= "OBS", family = "nbinom")

Thanks
very much for any assistance.  I appreciate your time and experience.

Carolina

Error
message:

> model1 <-
glmm.admb(InTOT ~  VELMEDIA + TEMP +
DIASULTLL + VVienM  + (1 | BLOQUE),
data=CAR08, group= "OBS", family = "nbinom")

 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3
3 3 3 3 3 3 4 4 4 4 4 4 4 4

Initial statistics: 6
variables; iteration 0; function evaluation 0

Function value  4.1326500e+002; maximum gradient component
mag -2.8449e+001

Var   Value   
Gradient   |Var   Value   
Gradient   |Var   Value   
Gradient   

  1 
0.0000 -2.8449e+001 |  2  0.0000 
1.5435e+000 |  3  0.0000 
2.0986e-001

  4 
0.0000  1.7579e-001 |  5 
0.0000  4.1826e-001 |  6 
0.0000 -2.8449e+001

 - final statistics:

6 variables; iteration 3;
function evaluation 7

Function value  7.2672e+000; maximum gradient component
mag  1.2679e-007

Exit code = 1;  converg criter  1.0000e-004

Var   Value   
Gradient   |Var   Value   
Gradient   |Var   Value   
Gradient   

  1 14.2247 
9.0376e-009 |  2 -1.5435  1.2679e-007 | 
3 -0.2099  1.7239e-008

  4 -0.1758 
1.4441e-008 |  5 -0.4183  3.4359e-008 | 
6 14.2247  9.0376e-009

 - final statistics:

6 variables; iteration 0;
function evaluation 0

Function value  7.2672e+000; maximum gradient component
mag  1.2679e-007

Exit code = 0;  converg criter  1.0000e-004

Var   Value   
Gradient   |Var   Value   
Gradient   |Var   Value   
Gradient   

  1 14.2247 
9.0376e-009 |  2 -1.5435  1.2679e-007 | 
3 -0.2099  1.7239e-008

  4 -0.1758 
1.4441e-008 |  5 -0.4183  3.4359e-008 | 
6 14.2247  9.0376e-009

 - final statistics:

6 variables; iteration 0;
function evaluation 0

Function value  7.2672e+000; maximum gradient component
mag  1.2679e-007

Exit code = 0;  converg criter  1.0000e-004

Var   Value   
Gradient   |Var   Value   
Gradient   |Var   Value   
Gradient   

  1 14.2247 
9.0376e-009 |  2 -1.5435  1.2679e-007 | 
3 -0.2099  1.7239e-008

  4 -0.1758 
1.4441e-008 |  5 -0.4183  3.4359e-008 | 
6 14.2247  9.0376e-009

Initial statistics: 6
variables; iteration 0; function evaluation 0

Function value  1.2243387e+001; maximum gradient component
mag  6.7672e-002

Var   Value   
Gradient   |Var   Value   
Gradient   |Var   Value   
Gradient   

  1 14.2247 
6.7672e-002 |  2 -1.5435  1.8390e-002 | 
3 -0.2099 -5.2429e-003

  4 -0.1758 
7.9414e-003 |  5 -0.4183
-2.4791e-002 |  6 14.2247  6.7672e-002

 - final statistics:

6 variables; iteration 3;
function evaluation 6

Function value  1.2242e+001; maximum gradient component mag
-8.1366e-007

Exit code = 1;  converg criter  1.0000e-004

Var   Value   
Gradient   |Var   Value   
Gradient   |Var   Value   
Gradient   

  1 14.2081 
1.0711e-007 |  2 -1.5524  4.1011e-008 | 
3 -0.2073 -8.1366e-007

  4 -0.1797 -5.4906e-007 |  5 -0.4061 -1.3473e-008 |  6 14.2081 
1.0711e-007

Initial statistics: 6
variables; iteration 0; function evaluation 0

Function value  2.9524069e+002; maximum gradient component
mag -2.9374e+000

Var   Value 
  Gradient   |Var  
Value    Gradient   |Var  
Value    Gradient   

  1 14.2081 -2.9374e+000 |  2 -1.5524 
1.8891e-001 |  3 -0.2073
-3.3111e-001

  4 -0.1797 -8.5760e-002 |  5 -0.4061 
1.0238e+000 |  6 14.2081
-2.9374e+000

 - final statistics:

6 variables; iteration 8;
function evaluation 12

Function value  2.9443e+002; maximum gradient component mag
-7.4780e-005

Exit code = 1;  converg criter  1.0000e-004

Var   Value   
Gradient   |Var   Value   
Gradient   |Var   Value   
Gradient   

  1 14.4567 -7.4780e-005 |  2 -1.5598 
9.0075e-006 |  3 -0.1646
-2.6415e-005

  4 -0.1616 
5.0054e-006 |  5 -0.5575
-6.2554e-005 |  6 14.4567 -7.4780e-005

Initial statistics: 7
variables; iteration 0; function evaluation 0

Function value  2.9442879e+002; maximum gradient component
mag  2.0682e+001

Var   Value   
Gradient   |Var   Value   
Gradient   |Var   Value   
Gradient   

  1 14.4567 -7.4780e-005 |  2 -1.5598 
9.0075e-006 |  3 -0.1646
-2.6415e-005

  4 -0.1616 
5.0054e-006 |  5 -0.5575
-6.2554e-005 |  6 14.4567 -7.4780e-005

  7  0.1759 
2.0682e+001 |

Intermediate statistics: 7
variables; iteration 10; function evaluation 13

Function value  2.9430495e+002; maximum gradient component
mag  1.7252e-003

Var   Value   
Gradient   |Var   Value   
Gradient   |Var   Value   
Gradient   

  1 14.4570 -5.6520e-006 |  2 -1.5566 
2.3429e-004 |  3 -0.1663  4.8452e-004

  4 -0.1625 
6.1462e-004 |  5 -0.5570  1.5608e-003 | 
6 14.4570 -5.6520e-006

  7  0.1639 
1.7252e-003 |

 - final statistics:

7 variables; iteration 12;
function evaluation 15

Function value  2.9430e+002; maximum gradient component
mag  9.6123e-005

Exit code = 1;  converg criter  1.0000e-004

Var   Value   
Gradient   |Var   Value   
Gradient   |Var   Value   
Gradient   

  1 14.4570 -3.6710e-006 |  2 -1.5566 -4.7519e-006 |  3 -0.1664 
7.1745e-006

  4 -0.1626 
1.0335e-006 |  5 -0.5573
-2.9225e-006 |  6 14.4570 -3.6710e-006

  7  0.1639 
9.6123e-005 |

Estimating row 1 out of 7 for
hessian

Estimating row 2 out of 7 for
hessian

Estimating row 3 out of 7 for
hessian

Estimating row 4 out of 7 for
hessian

Estimating row 5 out of 7 for
hessian

Estimating row 6 out of 7 for
hessian

Estimating row 7 out of 7 for
hessian

Warning -- Hessian does not
appear to be positive definite

Error en glmm.admb(InTOT ~
VELMEDIA + TEMP + DIASULTLL + VVienM + (1 | 
: 

  The
function maximizer failed

Además:
Mensajes de aviso perdidos

1: running command
'C:\Windows\system32\cmd.exe /c
"C:/PROGRA~1/R/R-212~1.1/library/glmmADMB/bin/windows/nbmm.exe" -maxfn
500 ' had status 1 

2: In shell(cmd, invisible =
TRUE) :

'"C:/PROGRA~1/R/R-212~1.1/library/glmmADMB/bin/windows/nbmm.exe"
-maxfn 500 ' execution failed with error code 1

Carolina Soto Navarro

JAE-Predoc. Fellowship
Doñana Biological station (CSIC)
Department of Conservation Biology
Avda. Américo Vespucio s/n
41092 Sevilla (Spain)
Tlf. 954 466 700

e-mail: carolina.soto at ebd.csic.es
http://www.ebd.csic.es/carnivoros/personal/soto/

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