### Estimation problems in multi-session analysis

Posted:

**Sun Aug 07, 2011 11:59 am**Hello,

This should be a relatively simple question but I have been struggling with it. I have a capthist object with 4 different sessions (each composed of 7 occasions), imported as required using a vector of trapfiles (one for each session):

RSData<-read.capthist(captfile="RSdata_LoggedForest.txt",trapfile=c("E1_RSTrapdata_Session1.txt","E2_RSTrapdata_Session2.txt","F1_RSTrapdata_Session3.txt","F2_RSTrapdata_Session4.txt"),detector="single")

When I inspect my data (summary) everything seems to be fine and all the sessions have been appropriately specified. However, when I try and run a simple model looking at density as a function of session, secr.fit returns the following output:

>RSsecr<-secr.fit(RSData,model=list(D~session,g0~1,sigma~1))

>RSsecr

secr.fit( capthist = RSData, model = list(D ~ session, g0 ~ 1, sigma ~ 1),

buffer = 50 )

secr 2.1.0, 15:47:17 07 Aug 2011

$`1`

Object class traps

Detector type single

Detector number 96

Average spacing 6.546681 m

x-range 876193.7 876324.4 m

y-range 521046.5 521316.3 m

$`2`

Object class traps

Detector type single

Detector number 96

Average spacing 7.640513 m

x-range 876302 876435 m

y-range 521490.1 521759.1 m

$`3`

Object class traps

Detector type single

Detector number 96

Average spacing 8.101695 m

x-range 871723.2 871813.8 m

y-range 520921.8 521185.1 m

$`4`

Object class traps

Detector type single

Detector number 96

Average spacing 7.708401 m

x-range 871764.1 871861 m

y-range 520466.6 520731 m

1 2 3 4

Occasions 7 7 7 7

Detections 72 57 78 57

Animals 29 26 36 28

Detectors 96 96 96 96

Model : D~session g0~1 sigma~1

Fixed (real) : none

Detection fn : halfnormal

Distribution : poisson

N parameters : 6

Log likelihood : -1e+10

AIC : 2e+10

AICc : 2e+10

Beta parameters (coefficients)

beta SE.beta lcl ucl

D 2.255436 NA NA NA

D.session2 0.000000 NA NA NA

D.session3 0.000000 NA NA NA

D.session4 0.000000 NA NA NA

g0 -1.714053 NA NA NA

sigma 2.494075 NA NA NA

Variance-covariance matrix of beta parameters

NULL

Fitted (real) parameters evaluated at base levels of covariates

session = 1

link estimate SE.estimate lcl ucl

D log 9.5394563 NA NA NA

g0 logit 0.1526388 NA NA NA

sigma log 12.1105241 NA NA NA

session = 2

link estimate SE.estimate lcl ucl

D log 9.5394563 NA NA NA

g0 logit 0.1526388 NA NA NA

sigma log 12.1105241 NA NA NA

session = 3

link estimate SE.estimate lcl ucl

D log 9.5394563 NA NA NA

g0 logit 0.1526388 NA NA NA

sigma log 12.1105241 NA NA NA

session = 4

link estimate SE.estimate lcl ucl

D log 9.5394563 NA NA NA

g0 logit 0.1526388 NA NA NA

sigma log 12.1105241 NA NA NA

It seems secr.fit is unable to estimate loglik properly and only considers the first session, even though it acknowledges the existence of 4 distinct sessions.

If someone could point out what I am doing wrong it would be really helpful! Sorry if this is obvious.

Many thanks!

Jeremy

This should be a relatively simple question but I have been struggling with it. I have a capthist object with 4 different sessions (each composed of 7 occasions), imported as required using a vector of trapfiles (one for each session):

RSData<-read.capthist(captfile="RSdata_LoggedForest.txt",trapfile=c("E1_RSTrapdata_Session1.txt","E2_RSTrapdata_Session2.txt","F1_RSTrapdata_Session3.txt","F2_RSTrapdata_Session4.txt"),detector="single")

When I inspect my data (summary) everything seems to be fine and all the sessions have been appropriately specified. However, when I try and run a simple model looking at density as a function of session, secr.fit returns the following output:

>RSsecr<-secr.fit(RSData,model=list(D~session,g0~1,sigma~1))

>RSsecr

secr.fit( capthist = RSData, model = list(D ~ session, g0 ~ 1, sigma ~ 1),

buffer = 50 )

secr 2.1.0, 15:47:17 07 Aug 2011

$`1`

Object class traps

Detector type single

Detector number 96

Average spacing 6.546681 m

x-range 876193.7 876324.4 m

y-range 521046.5 521316.3 m

$`2`

Object class traps

Detector type single

Detector number 96

Average spacing 7.640513 m

x-range 876302 876435 m

y-range 521490.1 521759.1 m

$`3`

Object class traps

Detector type single

Detector number 96

Average spacing 8.101695 m

x-range 871723.2 871813.8 m

y-range 520921.8 521185.1 m

$`4`

Object class traps

Detector type single

Detector number 96

Average spacing 7.708401 m

x-range 871764.1 871861 m

y-range 520466.6 520731 m

1 2 3 4

Occasions 7 7 7 7

Detections 72 57 78 57

Animals 29 26 36 28

Detectors 96 96 96 96

Model : D~session g0~1 sigma~1

Fixed (real) : none

Detection fn : halfnormal

Distribution : poisson

N parameters : 6

Log likelihood : -1e+10

AIC : 2e+10

AICc : 2e+10

Beta parameters (coefficients)

beta SE.beta lcl ucl

D 2.255436 NA NA NA

D.session2 0.000000 NA NA NA

D.session3 0.000000 NA NA NA

D.session4 0.000000 NA NA NA

g0 -1.714053 NA NA NA

sigma 2.494075 NA NA NA

Variance-covariance matrix of beta parameters

NULL

Fitted (real) parameters evaluated at base levels of covariates

session = 1

link estimate SE.estimate lcl ucl

D log 9.5394563 NA NA NA

g0 logit 0.1526388 NA NA NA

sigma log 12.1105241 NA NA NA

session = 2

link estimate SE.estimate lcl ucl

D log 9.5394563 NA NA NA

g0 logit 0.1526388 NA NA NA

sigma log 12.1105241 NA NA NA

session = 3

link estimate SE.estimate lcl ucl

D log 9.5394563 NA NA NA

g0 logit 0.1526388 NA NA NA

sigma log 12.1105241 NA NA NA

session = 4

link estimate SE.estimate lcl ucl

D log 9.5394563 NA NA NA

g0 logit 0.1526388 NA NA NA

sigma log 12.1105241 NA NA NA

It seems secr.fit is unable to estimate loglik properly and only considers the first session, even though it acknowledges the existence of 4 distinct sessions.

If someone could point out what I am doing wrong it would be really helpful! Sorry if this is obvious.

Many thanks!

Jeremy