[ADMB Users] gmml.admb error
H. Skaug
hskaug at gmail.com
Fri Apr 1 23:24:22 PDT 2011
Hi,
The (1 |BLOQUE) is not implemented, yet.
You still need to include a dummy "group", which can contain anything
as far as I remember, but it must be a vector of strings. You can
add a new variable to your dataset:
BLOQUE2$mygroup = rep("a",n)
where n is the number of observations, and use that as your "group".
Hans
On Fri, Apr 1, 2011 at 2:24 PM, Carolina Soto Navarro
<carota_soto at hotmail.com> wrote:
> 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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