I’m a pretty new user to RMark. For my bachelor thesis, I am using a ten year data set (2010, 2013, 2016, 2020) with 2 capture events each year in an open CJS model to estimate survival and capture probabilities and as well the population size of a toad population.
Everything worked out well expect for the survival estimation on the second date in 2016.
t5 has an estimate of nearly 1 and the lcl is 0 and the ucl 1.
The phitable:
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> phitable
all.diff.index par.index estimate se lcl
Phi g1 c1 a0 t1 1 1 0.8667496 0.0454135 7.506031e-01
Phi g1 c1 a1 t2 2 2 0.3533644 0.0181798 3.185974e-01
Phi g1 c1 a2 t3 3 3 0.8257282 0.0392040 7.352984e-01
Phi g1 c1 a3 t4 4 4 0.4642861 0.0396758 3.879939e-01
Phi g1 c1 a4 t5 5 5 0.9998393 0.0906375 3.461938e-305
Phi g1 c1 a5 t6 6 6 0.0441878 0.0111267 2.684490e-02
Phi g1 c1 a6 t7 7 7 0.3190024 0.0000000 3.190024e-01
Phi g1 c2 a0 t2 8 2 0.3533644 0.0181798 3.185974e-01
Phi g1 c2 a1 t3 9 3 0.8257282 0.0392040 7.352984e-01
Phi g1 c2 a2 t4 10 4 0.4642861 0.0396758 3.879939e-01
Phi g1 c2 a3 t5 11 5 0.9998393 0.0906375 3.461938e-305
Phi g1 c2 a4 t6 12 6 0.0441878 0.0111267 2.684490e-02
Phi g1 c2 a5 t7 13 7 0.3190024 0.0000000 3.190024e-01
Phi g1 c3 a0 t3 14 3 0.8257282 0.0392040 7.352984e-01
Phi g1 c3 a1 t4 15 4 0.4642861 0.0396758 3.879939e-01
Phi g1 c3 a2 t5 16 5 0.9998393 0.0906375 3.461938e-305
Phi g1 c3 a3 t6 17 6 0.0441878 0.0111267 2.684490e-02
Phi g1 c3 a4 t7 18 7 0.3190024 0.0000000 3.190024e-01
Phi g1 c4 a0 t4 19 4 0.4642861 0.0396758 3.879939e-01
Phi g1 c4 a1 t5 20 5 0.9998393 0.0906375 3.461938e-305
Phi g1 c4 a2 t6 21 6 0.0441878 0.0111267 2.684490e-02
Phi g1 c4 a3 t7 22 7 0.3190024 0.0000000 3.190024e-01
Phi g1 c5 a0 t5 23 5 0.9998393 0.0906375 3.461938e-305
Phi g1 c5 a1 t6 24 6 0.0441878 0.0111267 2.684490e-02
Phi g1 c5 a2 t7 25 7 0.3190024 0.0000000 3.190024e-01
Phi g1 c6 a0 t6 26 6 0.0441878 0.0111267 2.684490e-02
Phi g1 c6 a1 t7 27 7 0.3190024 0.0000000 3.190024e-01
Phi g1 c7 a0 t7 28 7 0.3190024 0.0000000 3.190024e-01
ucl fixed note group cohort age time occ.cohort Cohort
Phi g1 c1 a0 t1 0.9335911 1 1 0 1 1 0
Phi g1 c1 a1 t2 0.3897553 1 1 1 2 1 0
Phi g1 c1 a2 t3 0.8898908 1 1 2 3 1 0
Phi g1 c1 a3 t4 0.5422873 1 1 3 4 1 0
Phi g1 c1 a4 t5 1.0000000 1 1 4 5 1 0
Phi g1 c1 a5 t6 0.0719071 1 1 5 6 1 0
Phi g1 c1 a6 t7 0.3190024 1 1 6 7 1 0
Phi g1 c2 a0 t2 0.3897553 1 2 0 2 2 1
Phi g1 c2 a1 t3 0.8898908 1 2 1 3 2 1
Phi g1 c2 a2 t4 0.5422873 1 2 2 4 2 1
Phi g1 c2 a3 t5 1.0000000 1 2 3 5 2 1
Phi g1 c2 a4 t6 0.0719071 1 2 4 6 2 1
Phi g1 c2 a5 t7 0.3190024 1 2 5 7 2 1
Phi g1 c3 a0 t3 0.8898908 1 3 0 3 3 2
Phi g1 c3 a1 t4 0.5422873 1 3 1 4 3 2
Phi g1 c3 a2 t5 1.0000000 1 3 2 5 3 2
Phi g1 c3 a3 t6 0.0719071 1 3 3 6 3 2
Phi g1 c3 a4 t7 0.3190024 1 3 4 7 3 2
Phi g1 c4 a0 t4 0.5422873 1 4 0 4 4 3
Phi g1 c4 a1 t5 1.0000000 1 4 1 5 4 3
Phi g1 c4 a2 t6 0.0719071 1 4 2 6 4 3
Phi g1 c4 a3 t7 0.3190024 1 4 3 7 4 3
Phi g1 c5 a0 t5 1.0000000 1 5 0 5 5 4
Phi g1 c5 a1 t6 0.0719071 1 5 1 6 5 4
Phi g1 c5 a2 t7 0.3190024 1 5 2 7 5 4
Phi g1 c6 a0 t6 0.0719071 1 6 0 6 6 5
Phi g1 c6 a1 t7 0.3190024 1 6 1 7 6 5
Phi g1 c7 a0 t7 0.3190024 1 7 0 7 7 6
Let me walk you through what I have done:
- Code: Select all
str(Rmarkgbu)
'data.frame': 2321 obs. of 2 variables:
$ ch : chr "00100100" "01000000" "10000000" "00000001" ...
$ freq: num 1 1 1 1 1 1 1 1 1 1 ...
> summary(Rmarkgbu)
ch freq
Length:2321 Min. :1
Class :character 1st Qu.:1
Mode :character Median :1
Mean :1
3rd Qu.:1
Max. :1
> hw.proc <- process.data(Rmarkgbu, model="CJS")
> hw.ddl = make.design.data(hw.proc)
> release.gof(hw.proc)
RELEASE NORMAL TERMINATION
Chi.square df P
TEST2 0.9482 5 0.9666
TEST3 17.2108 11 0.1018
Total 18.1590 16 0.3147
> ###BUILDING MODELS
> # survival process
> Phi.ct = list(formula=~1) # constant
> Phi.time = list(formula=~time) # year effect
>
> # detection process
> p.ct = list(formula=~1) # constant
> p.time = list(formula=~time) # year effect
> # constant survival, constant recapture
Model.1 = mark(hw.proc,hw.ddl,model.parameters=list(Phi=Phi.ct,p=p.ct),output = FALSE,delete=T)
# constant survival, time-dependent recapture
Model.2 = mark(hw.proc,hw.ddl,model.parameters=list(Phi=Phi.ct,p=p.time),output = FALSE,delete=T)
# time-dependent survival, constant recapture
Model.3 = mark(hw.proc,hw.ddl,model.parameters=list(Phi=Phi.time,p=p.ct),output = FALSE,delete=T)
# time-dependent survival, time-dependent recapture
Model.4 = mark(hw.proc,hw.ddl,model.parameters=list(Phi=Phi.time,p=p.time),output = FALSE,delete=T)
Due to the low AICs, only the 4 model follows:
- Code: Select all
> # time-dependent survival, time-dependent recapture
> Model.4 = mark(hw.proc,hw.ddl,model.parameters=list(Phi=Phi.time,p=p.time),output = FALSE,delete=T)
The file is mark013.inp on unit 4.
Welcome to MARK monah on machine DESKTOP-0F4CH6H in subdirectory "C:\Dokumente\Uni\Institut Zoologie\Wildbook\Data Anaylse\2010-2020_Liekwegen" running file "mark013.inp".
INPUT --- proc title ;
CPU Time for the last procedure was 0.01 seconds.
INPUT --- proc chmatrix occasions= 8 groups= 1 etype= Live Nodes= 101
INPUT --- ICMeans NoHist hist= 69 ;
INPUT --- time interval 1 1 1 1 1 1 1 ;
INPUT --- glabel(1)=Group 1;
Number of unique encounter histories read was 69.
Number of individual covariates read was 0.
Time interval lengths are all equal to 1.
CPU Time for the last procedure was 0.01 seconds.
INPUT --- proc estimate link=Logit NOLOOP varest=2ndPart ;
Model is { Phi(~time)p(~time) }
INPUT --- model={ Phi(~time)p(~time) };
INPUT --- group=1 Phi rows=7 cols=7 Triang ;
INPUT --- 1 2 3 4 5 6 7 ;
INPUT --- 2 3 4 5 6 7 ;
INPUT --- 3 4 5 6 7 ;
INPUT --- 4 5 6 7 ;
INPUT --- 5 6 7 ;
INPUT --- 6 7 ;
INPUT --- 7 ;
INPUT --- group=1 p rows=7 cols=7 Triang ;
INPUT --- 8 9 10 11 12 13 14 ;
INPUT --- 9 10 11 12 13 14 ;
INPUT --- 10 11 12 13 14 ;
INPUT --- 11 12 13 14 ;
INPUT --- 12 13 14 ;
INPUT --- 13 14 ;
INPUT --- 14 ;
INPUT --- design matrix constraints=14 covariates=14;
INPUT --- 1 0 0 0 0 0 0 0 0 0 0 0 0 0;
INPUT --- 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
INPUT --- 1 0 1 0 0 0 0 0 0 0 0 0 0 0;
INPUT --- 1 0 0 1 0 0 0 0 0 0 0 0 0 0;
INPUT --- 1 0 0 0 1 0 0 0 0 0 0 0 0 0;
INPUT --- 1 0 0 0 0 1 0 0 0 0 0 0 0 0;
INPUT --- 1 0 0 0 0 0 1 0 0 0 0 0 0 0;
INPUT --- 0 0 0 0 0 0 0 1 0 0 0 0 0 0;
INPUT --- 0 0 0 0 0 0 0 1 1 0 0 0 0 0;
INPUT --- 0 0 0 0 0 0 0 1 0 1 0 0 0 0;
INPUT --- 0 0 0 0 0 0 0 1 0 0 1 0 0 0;
INPUT --- 0 0 0 0 0 0 0 1 0 0 0 1 0 0;
INPUT --- 0 0 0 0 0 0 0 1 0 0 0 0 1 0;
INPUT --- 0 0 0 0 0 0 0 1 0 0 0 0 0 1;
INPUT --- blabel(1)=Phi:(Intercept);
INPUT --- blabel(2)=Phi:time2;
INPUT --- blabel(3)=Phi:time3;
INPUT --- blabel(4)=Phi:time4;
INPUT --- blabel(5)=Phi:time5;
INPUT --- blabel(6)=Phi:time6;
INPUT --- blabel(7)=Phi:time7;
INPUT --- blabel(8)=p:(Intercept);
INPUT --- blabel(9)=p:time3;
INPUT --- blabel(10)=p:time4;
INPUT --- blabel(11)=p:time5;
INPUT --- blabel(12)=p:time6;
INPUT --- blabel(13)=p:time7;
INPUT --- blabel(14)=p:time8;
INPUT --- rlabel(1)=Phi g1 c1 a0 t1;
INPUT --- rlabel(2)=Phi g1 c1 a1 t2;
INPUT --- rlabel(3)=Phi g1 c1 a2 t3;
INPUT --- rlabel(4)=Phi g1 c1 a3 t4;
INPUT --- rlabel(5)=Phi g1 c1 a4 t5;
INPUT --- rlabel(6)=Phi g1 c1 a5 t6;
INPUT --- rlabel(7)=Phi g1 c1 a6 t7;
INPUT --- rlabel(8)=p g1 c1 a1 t2;
INPUT --- rlabel(9)=p g1 c1 a2 t3;
INPUT --- rlabel(10)=p g1 c1 a3 t4;
INPUT --- rlabel(11)=p g1 c1 a4 t5;
INPUT --- rlabel(12)=p g1 c1 a5 t6;
INPUT --- rlabel(13)=p g1 c1 a6 t7;
INPUT --- rlabel(14)=p g1 c1 a7 t8;
Model is { Ph{ Phi(~time)p(~time) } Iteration 138
CPU Time for numerical optimization was 0.28 seconds.
-2logL { Phi(~time)p(~time) } = 5420.4504
Penalty { Phi(~time)p(~time) } = 0.0000000
Gradient { Phi(~time)p(~time) }:
0.2124795E-04 0.1755496E-04 0.000000 -0.2023967E-04 0.000000
{ Phi(~time)p(~time) } VC Matrix: 100% done. CPU Time to compute VC matrix was 0.01 seconds.
CPU Time to invert VC matrix was 0.01 seconds.
Gap Method for Num. of Estimated Parameters { Phi(~time)p(~time) } = 12
Numerical Threshold Method for Num. of Estimated Parameters { Phi(~time)p(~time) } = 12
Number of Estimated Parameters { Phi(~time)p(~time) } = 12
DEVIANCE { Phi(~time)p(~time) } = 115.45050
DEVIANCE Degrees of Freedom { Phi(~time)p(~time) } = 49
c-hat { Phi(~time)p(~time) } = 2.3561326
AIC { Phi(~time)p(~time) } = 5444.4504
AICc { Phi(~time)p(~time) } = 5444.5394
BIC { Phi(~time)p(~time) } = 5518.4313
Pearson Chisquare { Phi(~time)p(~time) } = 219.02696
Possible Encounter Histories { Phi(~time)p(~time) } = 254
Pearson Chisquare df { Phi(~time)p(~time) } = 235
Pearson chat { Phi(~time)p(~time) } = 0.9320296
Sum(Observed/Expected) { Phi(~time)p(~time) } = 225.45989
s-bar { Phi(~time)p(~time) } = -0.1155470
Fletcher chat { Phi(~time)p(~time) } = 1.0537922
Beta number 5 is a singular value.
Note: only 12 parameters counted of 14 specified parameters
AICc and parameter count have been adjusted upward
42 Warnings
>
> summary(Model.1)$AICc
[1] 5971.911
> summary(Model.2)$AICc
[1] 5701.434
> summary(Model.3)$AICc
[1] 5481.055
> summary(Model.4)$AICc
[1] 5448.57
> PIMS(Model.4, "Phi")
group = Group 1
1 2 3 4 5 6 7
1 1 2 3 4 5 6 7
2 2 3 4 5 6 7
3 3 4 5 6 7
4 4 5 6 7
5 5 6 7
6 6 7
7 7
> PIMS(Model.4,"p")
group = Group 1
2 3 4 5 6 7 8
1 8 9 10 11 12 13 14
2 9 10 11 12 13 14
3 10 11 12 13 14
4 11 12 13 14
5 12 13 14
6 13 14
7 14
>
> #dimensions of design matrix
> dim(Model.4$design.matrix)
[1] 14 14
>
> #Design matrix for Phi portion
> Model.4$design.matrix[1:7,1:7]
Phi:(Intercept) Phi:time2 Phi:time3 Phi:time4 Phi:time5
Phi g1 c1 a0 t1 "1" "0" "0" "0" "0"
Phi g1 c1 a1 t2 "1" "1" "0" "0" "0"
Phi g1 c1 a2 t3 "1" "0" "1" "0" "0"
Phi g1 c1 a3 t4 "1" "0" "0" "1" "0"
Phi g1 c1 a4 t5 "1" "0" "0" "0" "1"
Phi g1 c1 a5 t6 "1" "0" "0" "0" "0"
Phi g1 c1 a6 t7 "1" "0" "0" "0" "0"
Phi:time6 Phi:time7
Phi g1 c1 a0 t1 "0" "0"
Phi g1 c1 a1 t2 "0" "0"
Phi g1 c1 a2 t3 "0" "0"
Phi g1 c1 a3 t4 "0" "0"
Phi g1 c1 a4 t5 "0" "0"
Phi g1 c1 a5 t6 "1" "0"
Phi g1 c1 a6 t7 "0" "1"
>
> #display estimates for this model and real parameter estimates.
> Model.4$results$beta
estimate se lcl ucl
Phi:(Intercept) 1.8725197 0.3932083 1.1018314 2.6432080
Phi:time2 -2.4768027 0.4308168 -3.3212036 -1.6324019
Phi:time3 -0.3168699 0.4783669 -1.2544690 0.6207291
Phi:time4 -2.0156188 0.4243319 -2.8473093 -1.1839283
Phi:time5 6.8635684 564.2640100 -1099.0939000 1112.8211000
Phi:time6 -4.9466326 0.4732863 -5.8742738 -4.0189914
Phi:time7 -2.6308800 0.0000000 -2.6308800 -2.6308800
p:(Intercept) 0.2916342 0.1500662 -0.0024956 0.5857639
p:time3 0.9746740 0.2167973 0.5497512 1.3995968
p:time4 0.2753713 0.2098636 -0.1359613 0.6867039
p:time5 0.1030551 0.2490068 -0.3849983 0.5911086
p:time6 -1.4341179 0.2151147 -1.8557428 -1.0124931
p:time7 0.1104052 0.5759449 -1.0184469 1.2392573
p:time8 1.4588605 0.0000000 1.4588605 1.4588605
> Model.4$results$real
estimate se lcl ucl fixed note
Phi g1 c1 a0 t1 0.8667496 0.0454135 7.506031e-01 0.9335911
Phi g1 c1 a1 t2 0.3533644 0.0181798 3.185974e-01 0.3897553
Phi g1 c1 a2 t3 0.8257282 0.0392040 7.352984e-01 0.8898908
Phi g1 c1 a3 t4 0.4642861 0.0396758 3.879939e-01 0.5422873
Phi g1 c1 a4 t5 0.9998393 0.0906375 3.461938e-305 1.0000000
Phi g1 c1 a5 t6 0.0441878 0.0111267 2.684490e-02 0.0719071
Phi g1 c1 a6 t7 0.3190024 0.0000000 3.190024e-01 0.3190024
p g1 c1 a1 t2 0.5723962 0.0367300 4.993761e-01 0.6423926
p g1 c1 a2 t3 0.7801101 0.0268397 7.230492e-01 0.8282079
p g1 c1 a3 t4 0.6380719 0.0338799 5.694139e-01 0.7015208
p g1 c1 a4 t5 0.5974110 0.0477915 5.013054e-01 0.6865754
p g1 c1 a5 t6 0.2418646 0.0282568 1.908463e-01 0.3014407
p g1 c1 a6 t7 0.5991775 0.1335434 3.345179e-01 0.8163633
p g1 c1 a7 t8 0.8520152 0.0000000 8.520152e-01 0.8520152
>run.gbu=function()
{
hw.proc <- process.data(Rmarkgbu, model="CJS")
hw.ddl = make.design.data(hw.proc)
Phi.ct = list(formula=~1)
Phi.time = list(formula=~time)
p.ct = list(formula=~1)
p.time = list(formula=~time)
model.list<-create.model.list("CJS")
results<-mark.wrapper(model.list,data=hw.proc,ddl=hw.ddl)
return(results )
}
> gbu.results<-run.gbu()
Phi.ct.p.ct
The file is mark013.inp on unit 4.
Welcome to MARK monah on machine DESKTOP-0F4CH6H in subdirectory "C:\Dokumente\Uni\Institut Zoologie\Wildbook\Data Anaylse\2010-2020_Liekwegen" running file "mark013.inp".
INPUT --- proc title ;
CPU Time for the last procedure was 0.01 seconds.
INPUT --- proc chmatrix occasions= 8 groups= 1 etype= Live Nodes= 101
INPUT --- ICMeans NoHist hist= 69 ;
INPUT --- time interval 1 1 1 1 1 1 1 ;
INPUT --- glabel(1)=Group 1;
Number of unique encounter histories read was 69.
Number of individual covariates read was 0.
Time interval lengths are all equal to 1.
CPU Time for the last procedure was 0.01 seconds.
INPUT --- proc estimate link=Logit NOLOOP varest=2ndPart ;
Model is { Phi(~1)p(~1) }
INPUT --- model={ Phi(~1)p(~1) };
INPUT --- group=1 Phi rows=7 cols=7 Triang ;
INPUT --- 1 1 1 1 1 1 1 ;
INPUT --- 1 1 1 1 1 1 ;
INPUT --- 1 1 1 1 1 ;
INPUT --- 1 1 1 1 ;
INPUT --- 1 1 1 ;
INPUT --- 1 1 ;
INPUT --- 1 ;
INPUT --- group=1 p rows=7 cols=7 Triang ;
INPUT --- 2 2 2 2 2 2 2 ;
INPUT --- 2 2 2 2 2 2 ;
INPUT --- 2 2 2 2 2 ;
INPUT --- 2 2 2 2 ;
INPUT --- 2 2 2 ;
INPUT --- 2 2 ;
INPUT --- 2 ;
INPUT --- design matrix constraints=2 covariates=2 identity;
INPUT --- blabel(1)=Phi:(Intercept);
INPUT --- blabel(2)=p:(Intercept);
INPUT --- rlabel(1)=Phi g1 c1 a0 t1;
INPUT --- rlabel(2)=p g1 c1 a1 t2;
Model is { Phi(~1)p(~1) }
Link Function Used is LOGIT
Variance Estimation Procedure Used is 2ndPart
-2logL(saturated) = 5304.999{ Phi(~1)p(~1) } Iteration 7
CPU Time for numerical optimization was 0.01 seconds.
-2logL { Phi(~1)p(~1) } = 5967.9078
Penalty { Phi(~1)p(~1) } = 0.0000000
Gradient { Phi(~1){ Phi(~1)p(~1) } VC Matrix: 100% done. CPU Time to compute VC matrix was 0.01 seconds.
CPU Time to invert VC matrix was 0.01 seconds.
Gap Method for Num. of Estimated Parameters { Phi(~1)p(~1) } = 2
Numerical Threshold Method for Num. of Estimated Parameters { Phi(~1)p(~1) } = 2
Number of Estimated Parameters { Phi(~1)p(~1) } = 2
DEVIANCE { Phi(~1)p(~1) } = 662.90794
DEVIANCE Degrees of Freedom { Phi(~1)p(~1) } = 59
c-hat { Phi(~1)p(~1) } = 11.235728
AIC { Phi(~1)p(~1) } = 5971.9078
AICc { Phi(~1)p(~1) } = 5971.9112
BIC { Phi(~1)p(~1) } = 5984.2380
Pearson Chisquare { Phi(~1)p(~1) } = 704.57224
Possible Encounter Histories { Phi(~1)p(~1) } = 254
Pearson Chisquare df { Phi(~1)p(~1) } = 245
Pearson chat { Phi(~1)p(~1) } = 2.8758051
Sum(Observed/Expected) { Phi(~1)p(~1) } = 187.88856
s-bar { Phi(~1)p(~1) } = -0.2676577
Fletcher chat { Phi(~1)p(~1) } = 3.9268590
CPU Time for the last procedure was 0.01 seconds.
INPUT --- proc stop;
CPU Time in minutes for this job was 0.00
Time Start = 02:03:22.188 Time End = 02:03:22.209
Wall Clock Time in minutes for this job was 0.00
STOP NORMAL EXIT
Output summary for CJS model
Name : Phi(~1)p(~1)
Npar : 2
-2lnL: 5967.908
AICc : 5971.911
Beta
estimate se lcl ucl
Phi:(Intercept) -0.0481868 0.0381038 -0.1228703 0.0264967
p:(Intercept) 0.6853685 0.0749680 0.5384312 0.8323058
Real Parameter Phi
1 2 3 4 5 6 7
1 0.4879556 0.4879556 0.4879556 0.4879556 0.4879556 0.4879556 0.4879556
2 0.4879556 0.4879556 0.4879556 0.4879556 0.4879556 0.4879556
3 0.4879556 0.4879556 0.4879556 0.4879556 0.4879556
4 0.4879556 0.4879556 0.4879556 0.4879556
5 0.4879556 0.4879556 0.4879556
6 0.4879556 0.4879556
7 0.4879556
Real Parameter p
2 3 4 5 6 7 8
1 0.6649358 0.6649358 0.6649358 0.6649358 0.6649358 0.6649358 0.6649358
2 0.6649358 0.6649358 0.6649358 0.6649358 0.6649358 0.6649358
3 0.6649358 0.6649358 0.6649358 0.6649358 0.6649358
4 0.6649358 0.6649358 0.6649358 0.6649358
5 0.6649358 0.6649358 0.6649358
6 0.6649358 0.6649358
7 0.6649358
Phi.time.p.ct
The file is mark014.inp on unit 4.
Welcome to MARK monah on machine DESKTOP-0F4CH6H in subdirectory "C:\Dokumente\Uni\Institut Zoologie\Wildbook\Data Anaylse\2010-2020_Liekwegen" running file "mark014.inp".
INPUT --- proc title ;
CPU Time for the last procedure was 0.01 seconds.
INPUT --- proc chmatrix occasions= 8 groups= 1 etype= Live Nodes= 101
INPUT --- ICMeans NoHist hist= 69 ;
INPUT --- time interval 1 1 1 1 1 1 1 ;
INPUT --- glabel(1)=Group 1;
Number of unique encounter histories read was 69.
Number of individual covariates read was 0.
Time interval lengths are all equal to 1.
CPU Time for the last procedure was 0.01 seconds.
INPUT --- proc estimate link=Logit NOLOOP varest=2ndPart ;
Model is { Phi(~time)p(~1) }
INPUT --- model={ Phi(~time)p(~1) };
INPUT --- group=1 Phi rows=7 cols=7 Triang ;
INPUT --- 1 2 3 4 5 6 7 ;
INPUT --- 2 3 4 5 6 7 ;
INPUT --- 3 4 5 6 7 ;
INPUT --- 4 5 6 7 ;
INPUT --- 5 6 7 ;
INPUT --- 6 7 ;
INPUT --- 7 ;
INPUT --- group=1 p rows=7 cols=7 Triang ;
INPUT --- 8 8 8 8 8 8 8 ;
INPUT --- 8 8 8 8 8 8 ;
INPUT --- 8 8 8 8 8 ;
INPUT --- 8 8 8 8 ;
INPUT --- 8 8 8 ;
INPUT --- 8 8 ;
INPUT --- 8 ;
INPUT --- design matrix constraints=8 covariates=8;
INPUT --- 1 0 0 0 0 0 0 0;
INPUT --- 1 1 0 0 0 0 0 0;
INPUT --- 1 0 1 0 0 0 0 0;
INPUT --- 1 0 0 1 0 0 0 0;
INPUT --- 1 0 0 0 1 0 0 0;
INPUT --- 1 0 0 0 0 1 0 0;
INPUT --- 1 0 0 0 0 0 1 0;
INPUT --- 0 0 0 0 0 0 0 1;
INPUT --- blabel(1)=Phi:(Intercept);
INPUT --- blabel(2)=Phi:time2;
INPUT --- blabel(3)=Phi:time3;
INPUT --- blabel(4)=Phi:time4;
INPUT --- blabel(5)=Phi:time5;
INPUT --- blabel(6)=Phi:time6;
INPUT --- blabel(7)=Phi:time7;
INPUT --- blabel(8)=p:(Intercept);
INPUT --- rlabel(1)=Phi g1 c1 a0 t1;
INPUT --- rlabel(2)=Phi g1 c1 a1 t2;
INPUT --- rlabel(3)=Phi g1 c1 a2 t3;
INPUT --- rlabel(4)=Phi g1 c1 a3 t4;
INPUT --- rlabel(5)=Phi g1 c1 a4 t5;
INPUT --- rlabel(6)=Phi g1 c1 a5 t6;
INPUT --- rlabel(7)=Phi g1 c1 a6 t7;
INPUT --- rlabel(8)=p g1 c1 a1 t2;
Model is { Phi(~time)p(~1) }
Link Function Used is LOGIT
Variance Estimation Procedure Used is 2ndPart
-2logL(sa{ Phi(~time)p(~1) } Iteration 22
CPU Time for numerical optimization was 0.02 seconds.
-2logL { Phi(~time)p(~1) } = 5465.0139
Penalty { Phi(~time)p(~1) } = 0.0000000
Gradient { Phi(~time)p(~1) }:
0.000000 0.000000 0.000000 0.00{ Phi(~time)p(~1) } VC Matrix: 100% done. CPU Time to compute VC matrix was 0.01 seconds.
CPU Time to invert VC matrix was 0.01 seconds.
Gap Method for Num. of Estimated Parameters { Phi(~time)p(~1) } = 8
Numerical Threshold Method for Num. of Estimated Parameters { Phi(~time)p(~1) } = 8
Number of Estimated Parameters { Phi(~time)p(~1) } = 8
DEVIANCE { Phi(~time)p(~1) } = 160.01401
DEVIANCE Degrees of Freedom { Phi(~time)p(~1) } = 53
c-hat { Phi(~time)p(~1) } = 3.0191322
AIC { Phi(~time)p(~1) } = 5481.0139
AICc { Phi(~time)p(~1) } = 5481.0549
BIC { Phi(~time)p(~1) } = 5530.3345
Pearson Chisquare { Phi(~time)p(~1) } = 443.00722
Possible Encounter Histories { Phi(~time)p(~1) } = 254
Pearson Chisquare df { Phi(~time)p(~1) } = 239
Pearson chat { Phi(~time)p(~1) } = 1.8535867
Sum(Observed/Expected) { Phi(~time)p(~1) } = 404.85811
s-bar { Phi(~time)p(~1) } = 0.6107616
Fletcher chat { Phi(~time)p(~1) } = 1.1507518
CPU Time for the last procedure was 0.02 seconds.
INPUT --- proc stop;
CPU Time in minutes for this job was 0.00
Time Start = 02:03:22.686 Time End = 02:03:22.799
Wall Clock Time in minutes for this job was 0.00
STOP NORMAL EXIT
Output summary for CJS model
Name : Phi(~time)p(~1)
Npar : 8
-2lnL: 5465.014
AICc : 5481.055
Beta
estimate se lcl ucl
Phi:(Intercept) 1.4073833 0.1980270 1.0192504 1.7955163
Phi:time2 -1.8603346 0.2232116 -2.2978293 -1.4228399
Phi:time3 -0.0859399 0.2408633 -0.5580320 0.3861522
Phi:time4 -1.6354728 0.2226990 -2.0719629 -1.1989827
Phi:time5 -1.7875486 0.2185353 -2.2158777 -1.3592194
Phi:time6 -3.8190833 0.2879576 -4.3834802 -3.2546864
Phi:time7 -1.7221837 0.2703487 -2.2520671 -1.1923002
p:(Intercept) 0.6186704 0.0765595 0.4686138 0.7687271
Real Parameter Phi
1 2 3 4 5 6 7
1 0.8033529 0.3886593 0.7894218 0.4432236 0.406087 0.0822849 0.4219435
2 0.3886593 0.7894218 0.4432236 0.406087 0.0822849 0.4219435
3 0.7894218 0.4432236 0.406087 0.0822849 0.4219435
4 0.4432236 0.406087 0.0822849 0.4219435
5 0.406087 0.0822849 0.4219435
6 0.0822849 0.4219435
7 0.4219435
Real Parameter p
2 3 4 5 6 7 8
1 0.6499161 0.6499161 0.6499161 0.6499161 0.6499161 0.6499161 0.6499161
2 0.6499161 0.6499161 0.6499161 0.6499161 0.6499161 0.6499161
3 0.6499161 0.6499161 0.6499161 0.6499161 0.6499161
4 0.6499161 0.6499161 0.6499161 0.6499161
5 0.6499161 0.6499161 0.6499161
6 0.6499161 0.6499161
7 0.6499161
Phi.ct.p.time
The file is mark015.inp on unit 4.
Welcome to MARK monah on machine DESKTOP-0F4CH6H in subdirectory "C:\Dokumente\Uni\Institut Zoologie\Wildbook\Data Anaylse\2010-2020_Liekwegen" running file "mark015.inp".
INPUT --- proc title ;
CPU Time for the last procedure was 0.01 seconds.
INPUT --- proc chmatrix occasions= 8 groups= 1 etype= Live Nodes= 101
INPUT --- ICMeans NoHist hist= 69 ;
INPUT --- time interval 1 1 1 1 1 1 1 ;
INPUT --- glabel(1)=Group 1;
Number of unique encounter histories read was 69.
Number of individual covariates read was 0.
Time interval lengths are all equal to 1.
CPU Time for the last procedure was 0.01 seconds.
INPUT --- proc estimate link=Logit NOLOOP varest=2ndPart ;
Model is { Phi(~1)p(~time) }
INPUT --- model={ Phi(~1)p(~time) };
INPUT --- group=1 Phi rows=7 cols=7 Triang ;
INPUT --- 1 1 1 1 1 1 1 ;
INPUT --- 1 1 1 1 1 1 ;
INPUT --- 1 1 1 1 1 ;
INPUT --- 1 1 1 1 ;
INPUT --- 1 1 1 ;
INPUT --- 1 1 ;
INPUT --- 1 ;
INPUT --- group=1 p rows=7 cols=7 Triang ;
INPUT --- 2 3 4 5 6 7 8 ;
INPUT --- 3 4 5 6 7 8 ;
INPUT --- 4 5 6 7 8 ;
INPUT --- 5 6 7 8 ;
INPUT --- 6 7 8 ;
INPUT --- 7 8 ;
INPUT --- 8 ;
INPUT --- design matrix constraints=8 covariates=8;
INPUT --- 1 0 0 0 0 0 0 0;
INPUT --- 0 1 0 0 0 0 0 0;
INPUT --- 0 1 1 0 0 0 0 0;
INPUT --- 0 1 0 1 0 0 0 0;
INPUT --- 0 1 0 0 1 0 0 0;
INPUT --- 0 1 0 0 0 1 0 0;
INPUT --- 0 1 0 0 0 0 1 0;
INPUT --- 0 1 0 0 0 0 0 1;
INPUT --- blabel(1)=Phi:(Intercept);
INPUT --- blabel(2)=p:(Intercept);
INPUT --- blabel(3)=p:time3;
INPUT --- blabel(4)=p:time4;
INPUT --- blabel(5)=p:time5;
INPUT --- blabel(6)=p:time6;
INPUT --- blabel(7)=p:time7;
INPUT --- blabel(8)=p:time8;
INPUT --- rlabel(1)=Phi g1 c1 a0 t1;
INPUT --- rlabel(2)=p g1 c1 a1 t2;
INPUT --- rlabel(3)=p g1 c1 a2 t3;
INPUT --- rlabel(4)=p g1 c1 a3 t4;
INPUT --- rlabel(5)=p g1 c1 a4 t5;
INPUT --- rlabel(6)=p g1 c1 a5 t6;
INPUT --- rlabel(7)=p g1 c1 a6 t7;
INPUT --- rlabel(8)=p g1 c1 a7 t8;
Model is { Phi(~1)p(~time) }
Link Function Used is LOGIT
Variance Estimation Procedure Used is 2ndPart
-2logL(sa{ Phi(~1)p(~time) } Iteration 22
CPU Time for numerical optimization was 0.02 seconds.
-2logL { Phi(~1)p(~time) } = 5685.3929
Penalty { Phi(~1)p(~time) } = 0.0000000
Gradient { Phi(~1)p(~time) }:
-0.4764727E-04 -0.3050278E-04 0.000000 -0.588{ Phi(~1)p(~time) } VC Matrix: 100% done. CPU Time to compute VC matrix was 0.01 seconds.
CPU Time to invert VC matrix was 0.01 seconds.
Gap Method for Num. of Estimated Parameters { Phi(~1)p(~time) } = 8
Numerical Threshold Method for Num. of Estimated Parameters { Phi(~1)p(~time) } = 8
Number of Estimated Parameters { Phi(~1)p(~time) } = 8
DEVIANCE { Phi(~1)p(~time) } = 380.39306
DEVIANCE Degrees of Freedom { Phi(~1)p(~time) } = 53
c-hat { Phi(~1)p(~time) } = 7.1772275
AIC { Phi(~1)p(~time) } = 5701.3929
AICc { Phi(~1)p(~time) } = 5701.4340
BIC { Phi(~1)p(~time) } = 5750.7136
Pearson Chisquare { Phi(~1)p(~time) } = 624.51674
Possible Encounter Histories { Phi(~1)p(~time) } = 254
Pearson Chisquare df { Phi(~1)p(~time) } = 239
Pearson chat { Phi(~1)p(~time) } = 2.6130408
Sum(Observed/Expected) { Phi(~1)p(~time) } = 368.46712
s-bar { Phi(~1)p(~time) } = 0.4634296
Fletcher chat { Phi(~1)p(~time) } = 1.7855595
CPU Time for the last procedure was 0.03 seconds.
INPUT --- proc stop;
CPU Time in minutes for this job was 0.00
Time Start = 02:03:23.075 Time End = 02:03:23.181
Wall Clock Time in minutes for this job was 0.00
STOP NORMAL EXIT
Output summary for CJS model
Name : Phi(~1)p(~time)
Npar : 8
-2lnL: 5685.393
AICc : 5701.434
Beta
estimate se lcl ucl
Phi:(Intercept) 0.2809791 0.0471769 0.1885125 0.3734458
p:(Intercept) 1.0009703 0.1361386 0.7341386 1.2678020
p:time3 -0.5151322 0.1786958 -0.8653759 -0.1648885
p:time4 0.0368232 0.1836453 -0.3231216 0.3967681
p:time5 -0.7418467 0.1957555 -1.1255276 -0.3581659
p:time6 -1.3233191 0.1863139 -1.6884944 -0.9581438
p:time7 -3.5195783 0.2676317 -4.0441365 -2.9950202
p:time8 -2.3035386 0.2176624 -2.7301570 -1.8769202
Real Parameter Phi
1 2 3 4 5 6 7
1 0.5697863 0.5697863 0.5697863 0.5697863 0.5697863 0.5697863 0.5697863
2 0.5697863 0.5697863 0.5697863 0.5697863 0.5697863 0.5697863
3 0.5697863 0.5697863 0.5697863 0.5697863 0.5697863
4 0.5697863 0.5697863 0.5697863 0.5697863
5 0.5697863 0.5697863 0.5697863
6 0.5697863 0.5697863
7 0.5697863
Real Parameter p
2 3 4 5 6 7 8
1 0.7312493 0.6191255 0.738424 0.5644208 0.4201034 0.0745639 0.2137331
2 0.6191255 0.738424 0.5644208 0.4201034 0.0745639 0.2137331
3 0.738424 0.5644208 0.4201034 0.0745639 0.2137331
4 0.5644208 0.4201034 0.0745639 0.2137331
5 0.4201034 0.0745639 0.2137331
6 0.0745639 0.2137331
7 0.2137331
Phi.time.p.tim
The file is mark016.inp on unit 4.
Welcome to MARK monah on machine DESKTOP-0F4CH6H in subdirectory "C:\Dokumente\Uni\Institut Zoologie\Wildbook\Data Anaylse\2010-2020_Liekwegen" running file "mark016.inp".
INPUT --- proc title ;
CPU Time for the last procedure was 0.01 seconds.
INPUT --- proc chmatrix occasions= 8 groups= 1 etype= Live Nodes= 101
INPUT --- ICMeans NoHist hist= 69 ;
INPUT --- time interval 1 1 1 1 1 1 1 ;
INPUT --- glabel(1)=Group 1;
Number of unique encounter histories read was 69.
Number of individual covariates read was 0.
Time interval lengths are all equal to 1.
CPU Time for the last procedure was 0.01 seconds.
INPUT --- proc estimate link=Logit NOLOOP varest=2ndPart ;
Model is { Phi(~time)p(~time) }
INPUT --- model={ Phi(~time)p(~time) };
INPUT --- group=1 Phi rows=7 cols=7 Triang ;
INPUT --- 1 2 3 4 5 6 7 ;
INPUT --- 2 3 4 5 6 7 ;
INPUT --- 3 4 5 6 7 ;
INPUT --- 4 5 6 7 ;
INPUT --- 5 6 7 ;
INPUT --- 6 7 ;
INPUT --- 7 ;
INPUT --- group=1 p rows=7 cols=7 Triang ;
INPUT --- 8 9 10 11 12 13 14 ;
INPUT --- 9 10 11 12 13 14 ;
INPUT --- 10 11 12 13 14 ;
INPUT --- 11 12 13 14 ;
INPUT --- 12 13 14 ;
INPUT --- 13 14 ;
INPUT --- 14 ;
INPUT --- design matrix constraints=14 covariates=14;
INPUT --- 1 0 0 0 0 0 0 0 0 0 0 0 0 0;
INPUT --- 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
INPUT --- 1 0 1 0 0 0 0 0 0 0 0 0 0 0;
INPUT --- 1 0 0 1 0 0 0 0 0 0 0 0 0 0;
INPUT --- 1 0 0 0 1 0 0 0 0 0 0 0 0 0;
INPUT --- 1 0 0 0 0 1 0 0 0 0 0 0 0 0;
INPUT --- 1 0 0 0 0 0 1 0 0 0 0 0 0 0;
INPUT --- 0 0 0 0 0 0 0 1 0 0 0 0 0 0;
INPUT --- 0 0 0 0 0 0 0 1 1 0 0 0 0 0;
INPUT --- 0 0 0 0 0 0 0 1 0 1 0 0 0 0;
INPUT --- 0 0 0 0 0 0 0 1 0 0 1 0 0 0;
INPUT --- 0 0 0 0 0 0 0 1 0 0 0 1 0 0;
INPUT --- 0 0 0 0 0 0 0 1 0 0 0 0 1 0;
INPUT --- 0 0 0 0 0 0 0 1 0 0 0 0 0 1;
INPUT --- blabel(1)=Phi:(Intercept);
INPUT --- blabel(2)=Phi:time2;
INPUT --- blabel(3)=Phi:time3;
INPUT --- blabel(4)=Phi:time4;
INPUT --- blabel(5)=Phi:time5;
INPUT --- blabel(6)=Phi:time6;
INPUT --- blabel(7)=Phi:time7;
INPUT --- blabel(8)=p:(Intercept);
INPUT --- blabel(9)=p:time3;
INPUT --- blabel(10)=p:time4;
INPUT --- blabel(11)=p:time5;
INPUT --- blabel(12)=p:time6;
INPUT --- blabel(13)=p:time7;
INPUT --- blabel(14)=p:time8;
INPUT --- rlabel(1)=Phi g1 c1 a0 t1;
INPUT --- rlabel(2)=Phi g1 c1 a1 t2;
INPUT --- rlabel(3)=Phi g1 c1 a2 t3;
INPUT --- rlabel(4)=Phi g1 c1 a3 t4;
INPUT --- rlabel(5)=Phi g1 c1 a4 t5;
INPUT --- rlabel(6)=Phi g1 c1 a5 t6;
INPUT --- rlabel(7)=Phi g1 c1 a6 t7;
INPUT --- rlabel(8)=p g1 c1 a1 t2;
INPUT --- rlabel(9)=p g1 c1 a2 t3;
INPUT --- rlabel(10)=p g1 c1 a3 t4;
INPUT --- rlabel(11)=p g1 c1 a4 t5;
INPUT --- rlabel(12)=p g1 c1 a5 t6;
INPUT --- rlabel(13)=p g1 c1 a6 t7;
INPUT --- rlabel(14)=p g1 c1 a7 t8;
Model is { Ph{ Phi(~time)p(~time) } Iteration 138
CPU Time for numerical optimization was 0.27 seconds.
-2logL { Phi(~time)p(~time) } = 5420.4504
Penalty { Phi(~time)p(~time) } = 0.0000000
Gradient { Phi(~time)p(~time) }:
0.2124795E-04 0.1755496E-04 0.000000 -0.2023967E-04 0.000000
{ Phi(~time)p(~time) } VC Matrix: 100% done. CPU Time to compute VC matrix was 0.08 seconds.
CPU Time to invert VC matrix was 0.01 seconds.
Gap Method for Num. of Estimated Parameters { Phi(~time)p(~time) } = 12
Numerical Threshold Method for Num. of Estimated Parameters { Phi(~time)p(~time) } = 12
Number of Estimated Parameters { Phi(~time)p(~time) } = 12
DEVIANCE { Phi(~time)p(~time) } = 115.45050
DEVIANCE Degrees of Freedom { Phi(~time)p(~time) } = 49
c-hat { Phi(~time)p(~time) } = 2.3561326
AIC { Phi(~time)p(~time) } = 5444.4504
AICc { Phi(~time)p(~time) } = 5444.5394
BIC { Phi(~time)p(~time) } = 5518.4313
Pearson Chisquare { Phi(~time)p(~time) } = 219.02696
Possible Encounter Histories { Phi(~time)p(~time) } = 254
Pearson Chisquare df { Phi(~time)p(~time) } = 235
Pearson chat { Phi(~time)p(~time) } = 0.9320296
Sum(Observed/Expected) { Phi(~time)p(~time) } = 225.45989
s-bar { Phi(~time)p(~time) } = -0.1155470
Fletcher chat { Phi(~time)p(~time) } = 1.0537922
Beta number 5 is a singular value.
CPU Time for the last procedure was 0.34 seconds.
INPUT --- proc stop;
CPU Time in minutes for this job was 0.01
Time Start = 02:03:23.513 Time End = 02:03:23.839
Wall Clock Time in minutes for this job was 0.01
STOP NORMAL EXIT
Note: only 12 parameters counted of 14 specified parameters
AICc and parameter count have been adjusted upward
Output summary for CJS model
Name : Phi(~time)p(~time
Npar : 14 (unadjusted=12)
-2lnL: 5420.45
AICc : 5448.57 (unadjusted=5444.5394)
Beta
estimate se lcl ucl
Phi:(Intercept) 1.8725197 0.3932083 1.1018314 2.6432080
Phi:time2 -2.4768027 0.4308168 -3.3212036 -1.6324019
Phi:time3 -0.3168699 0.4783669 -1.2544690 0.6207291
Phi:time4 -2.0156188 0.4243319 -2.8473093 -1.1839283
Phi:time5 6.8635684 564.2640100 -1099.0939000 1112.8211000
Phi:time6 -4.9466326 0.4732863 -5.8742738 -4.0189914
Phi:time7 -2.6308800 0.0000000 -2.6308800 -2.6308800
p:(Intercept) 0.2916342 0.1500662 -0.0024956 0.5857639
p:time3 0.9746740 0.2167973 0.5497512 1.3995968
p:time4 0.2753713 0.2098636 -0.1359613 0.6867039
p:time5 0.1030551 0.2490068 -0.3849983 0.5911086
p:time6 -1.4341179 0.2151147 -1.8557428 -1.0124931
p:time7 0.1104052 0.5759449 -1.0184469 1.2392573
p:time8 1.4588605 0.0000000 1.4588605 1.4588605
Real Parameter Phi
1 2 3 4 5 6 7
1 0.8667496 0.3533644 0.8257282 0.4642861 0.9998393 0.0441878 0.3190024
2 0.3533644 0.8257282 0.4642861 0.9998393 0.0441878 0.3190024
3 0.8257282 0.4642861 0.9998393 0.0441878 0.3190024
4 0.4642861 0.9998393 0.0441878 0.3190024
5 0.9998393 0.0441878 0.3190024
6 0.0441878 0.3190024
7 0.3190024
Real Parameter p
2 3 4 5 6 7 8
1 0.5723962 0.7801101 0.6380719 0.597411 0.2418646 0.5991775 0.8520152
2 0.7801101 0.6380719 0.597411 0.2418646 0.5991775 0.8520152
3 0.6380719 0.597411 0.2418646 0.5991775 0.8520152
4 0.597411 0.2418646 0.5991775 0.8520152
5 0.2418646 0.5991775 0.8520152
6 0.5991775 0.8520152
7 0.8520152
Es gab 50 oder mehr Warnungen (Anzeige der ersten 50 mit warnings())
> gbu.results
model npar AICc DeltaAICc weight Deviance
4 Phi(~time)p(~time) 14 5448.570 0.0000 9.999999e-01 115.4505
3 Phi(~time)p(~1) 8 5481.055 32.4846 8.832015e-08 160.0140
2 Phi(~1)p(~time) 8 5701.434 252.8636 0.000000e+00 380.3931
1 Phi(~1)p(~1) 2 5971.911 523.3409 0.000000e+00 662.9079
> summary(gbu.results)
Length Class Mode
Phi.ct.p.ct 25 mark list
Phi.ct.p.time 25 mark list
Phi.time.p.ct 25 mark list
Phi.time.p.time 25 mark list
model.table 8 data.frame list
>
> gbu.results$Model.4$results$real
NULL
###TABLE OF ESTIMATES
#get real estimates under Phi p model
> phitable = get.real(Model.4,"Phi", se= TRUE)
> phitable
> phitable[c("estimate","se","lcl","ucl")]
I know that Beta number 5 is a singular value and the estimation of phi 5 is weird. As well as there just have been 12 parameters out of 14 been taken into account. I have already tried to adjust the parameters, but always fail. I don't know how to code it.
I am not sure what I can do, to get reliable results for the survival probability of the individuals from the first date to the second monitoring date in 2016.
I have read all the articles and papers and also the guide, but unfortunately I am getting nowhere and have no one to ask.
Any information and help is greatly appreciated!