JSSAsecrD - openCR 2.2.1
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Gabrielle Beca
Nov 23, 2021, 3:05:25 AM11/23/21
to secr
Hi everyone,
I'm using openCR (2.2.1) to estimate D of Northern quolls from 9 sections with variable trap usage. I'm trying to use JSSAsecrD models but they keep failing. That's my code:
quoll_f <- read.capthist(captfile = "quolls_capturedata_f.txt",
trapfile = list ("2011_traplocs.txt",
"2014_traplocs.txt",
"2015_traplocs.txt",
"2016_traplocs.txt",
"2017_traplocs.txt",
"2018_traplocs.txt",
"2019_traplocs.txt",
"2020_traplocs.txt",
"2021_traplocs.txt"),
detector = "multi",
fmt = c("trapID","XY"),
verify = TRUE)
mask_quoll_f <- make.mask (traps(quoll_f), buffer = 450, type = 'trapbuffer')
intervals(quoll_f)<-c(36,12,12,12,12,12,12,12)/12
summary(quoll_f)
RPSV(quoll_f,CC = TRUE)
RPSV(quoll_f,CC = TRUE)
$`1`
[1] 85.41759
$`2`
[1] 83.40466
$`3`
[1] 74.4249
$`4`
[1] 110.6227
$`5`
[1] 77.7946
$`6`
[1] NaN
$`7`
[1] 59.57558
$`8`
[1] 54.9295
$`9`
[1] 64.75724
mask_quoll_f <- make.mask (traps(quoll_f), buffer = 450, type = 'trapbuffer')
quoll_model_f <- openCR.fit(quoll_f, type = 'JSSAsecrD',mask=mask_quoll_f,
model=list(sigma~t,lambda0~t,phi~t, D~t))
Capthist summary is not revealing any errors and all the rest looks fine. I wonder if there is anything basic that I'm missing...
N animals : 19
N detections : 51
N sessions : 9 (secondary 54)
Intervals : 0 0 0 0 0 0 3 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0
Analysis type : JSSAsecrD
Model : lambda0~t phi~t D~t sigma~t
Fixed (real) : none
Movement model : static
N parameters : 35
Log likelihood : 4e+10
AIC : -8e+10
AICc : NA
Parameter Link
lambda0 log
phi logit
D log
sigma log
Beta parameters (coefficients)
beta SE.beta lcl ucl
lambda0 -6.203135373 0 -6.203135373 -6.203135373
lambda0.t2 -0.303866978 0 -0.303866978 -0.303866978
lambda0.t3 -0.579951665 0 -0.579951665 -0.579951665
lambda0.t4 -0.306556062 0 -0.306556062 -0.306556062
lambda0.t5 -0.059169945 0 -0.059169945 -0.059169945
lambda0.t6 -0.082609665 0 -0.082609665 -0.082609665
lambda0.t7 -0.033335454 0 -0.033335454 -0.033335454
lambda0.t8 -0.051821369 0 -0.051821369 -0.051821369
lambda0.t9 -0.064279590 0 -0.064279590 -0.064279590
phi 1.558678518 0 1.558678518 1.558678518
phi.t2 0.065484080 0 0.065484080 0.065484080
phi.t3 0.078539555 0 0.078539555 0.078539555
phi.t4 0.061121671 0 0.061121671 0.061121671
phi.t5 0.089309085 0 0.089309085 0.089309085
phi.t6 0.099340242 0 0.099340242 0.099340242
phi.t7 0.096221697 0 0.096221697 0.096221697
phi.t8 0.108237779 0 0.108237779 0.108237779
D -4.124766944 0 -4.124766944 -4.124766944
D.t2 -0.166545820 0 -0.166545820 -0.166545820
D.t3 0.001148656 0 0.001148656 0.001148656
D.t4 -0.165479142 0 -0.165479142 -0.165479142
D.t5 -0.169054891 0 -0.169054891 -0.169054891
D.t6 -0.171239887 0 -0.171239887 -0.171239887
D.t7 -0.150845091 0 -0.150845091 -0.150845091
D.t8 -0.158959095 0 -0.158959095 -0.158959095
D.t9 -0.497977849 0 -0.497977849 -0.497977849
sigma 15.586927067 0 15.586927067 15.586927067
sigma.t2 0.778769390 0 0.778769390 0.778769390
sigma.t3 4.108629567 0 4.108629567 4.108629567
sigma.t4 0.026398046 0 0.026398046 0.026398046
sigma.t5 -0.053484310 0 -0.053484310 -0.053484310
sigma.t6 -0.058172805 0 -0.058172805 -0.058172805
sigma.t7 -0.013939677 0 -0.013939677 -0.013939677
sigma.t8 4.018734506 0 4.018734506 4.018734506
sigma.t9 1.857914515 0 1.857914515 1.857914515
Eigenvalues : NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Numerical rank of Hessian : 0 ( svtol = 1e-05 )
Warning: at least one real parameter is not identifiable
Variance-covariance matrix of beta parameters
lambda0 lambda0.t2 lambda0.t3 lambda0.t4 lambda0.t5 lambda0.t6 lambda0.t7 lambda0.t8 lambda0.t9 phi phi.t2 phi.t3 phi.t4
lambda0 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t2 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t3 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t4 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t5 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t6 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t7 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t8 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t9 0 0 0 0 0 0 0 0 0 0 0 0 0
phi 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t2 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t3 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t4 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t5 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t6 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t7 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t8 0 0 0 0 0 0 0 0 0 0 0 0 0
D 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t2 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t3 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t4 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t5 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t6 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t7 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t8 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t9 0 0 0 0 0 0 0 0 0 0 0 0 0
sigma 0 0 0 0 0 0 0 0 0 0 0 0 0
sigma.t2 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t5 phi.t6 phi.t7 phi.t8 D D.t2 D.t3 D.t4 D.t5 D.t6 D.t7 D.t8 D.t9 sigma sigma.t2 sigma.t3 sigma.t4 sigma.t5 sigma.t6
lambda0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
lambda0.t9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phi.t8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
D 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
D.t9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sigma 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sigma.t2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sigma.t7 sigma.t8 sigma.t9
lambda0 0 0 0
lambda0.t2 0 0 0
lambda0.t3 0 0 0
lambda0.t4 0 0 0
lambda0.t5 0 0 0
lambda0.t6 0 0 0
lambda0.t7 0 0 0
lambda0.t8 0 0 0
lambda0.t9 0 0 0
phi 0 0 0
phi.t2 0 0 0
phi.t3 0 0 0
phi.t4 0 0 0
phi.t5 0 0 0
phi.t6 0 0 0
phi.t7 0 0 0
phi.t8 0 0 0
D 0 0 0
D.t2 0 0 0
D.t3 0 0 0
D.t4 0 0 0
D.t5 0 0 0
D.t6 0 0 0
D.t7 0 0 0
D.t8 0 0 0
D.t9 0 0 0
sigma 0 0 0
sigma.t2 0 0 0
[ reached getOption("max.print") -- omitted 7 rows ]
Fitted (real) parameters evaluated at base levels of covariates
lambda0
session estimate SE.estimate lcl ucl
1 0.002023078 0 0.002023078 0.002023078
2 0.001492948 0 0.001492948 0.001492948
3 0.001132773 0 0.001132773 0.001132773
4 0.001488939 0 0.001488939 0.001488939
5 0.001906845 0 0.001906845 0.001906845
6 0.001862669 0 0.001862669 0.001862669
7 0.001956749 0 0.001956749 0.001956749
8 0.001920909 0 0.001920909 0.001920909
9 0.001897126 0 0.001897126 0.001897126
phi
session estimate SE.estimate lcl ucl
1 0.8261636 0 0.8261636 0.8261636
2 0.8353684 0 0.8353684 0.8353684
3 0.8371560 0 0.8371560 0.8371560
4 0.8347676 0 0.8347676 0.8347676
5 0.8386189 0 0.8386189 0.8386189
6 0.8399719 0 0.8399719 0.8399719
7 0.8395522 0 0.8395522 0.8395522
8 0.8411643 0 0.8411643 0.8411643
9 NA NA NA NA
D
session estimate SE.estimate lcl ucl
1 0.016167262 0 0.016167262 0.016167262
2 0.013686946 0 0.013686946 0.013686946
3 0.016185843 0 0.016185843 0.016185843
4 0.013701553 0 0.013701553 0.013701553
5 0.013652647 0 0.013652647 0.013652647
6 0.013622849 0 0.013622849 0.013622849
7 0.013903537 0 0.013903537 0.013903537
8 0.013791180 0 0.013791180 0.013791180
9 0.009825789 0 0.009825789 0.009825789
sigma
session estimate SE.estimate lcl ucl
1 5879175 0 5879175 5879175
2 12809485 0 12809485 12809485
3 357825728 0 357825728 357825728
4 6036441 0 6036441 6036441
5 5572993 0 5572993 5572993
6 5546925 0 5546925 5546925
7 5797790 0 5797790 5797790
8 327062412 0 327062412 327062412
9 37687596 0 37687596 37687596
Capthist summary is not revealing any errors and all the rest looks fine. I wonder if there is anything basic that I'm missing...
Super appreciate any help you can provide :)
Thanks,
Thanks,
Gabrielle
Murray Efford
Nov 23, 2021, 3:51:33 AM11/23/21
to secr
A model with that many parameters needs (much) more data. Start with a constant model. My own preference is to fit PLB models first and worry about density later, if at all. Also, you seem to have no recaptures (or no spatial recaptures) in session 6, so no info to support session-specific estimates of detection parameters in session 6 (it would help to see the output from summary(quoll_f, terse = T) and perhaps m.array(quoll_f)). There may be other problems, but you should start small.
Gabrielle Beca
Nov 24, 2021, 1:53:32 AM11/24/21
to secr
Hi Murray,
Thanks for any help you can provide,
Thanks for your email :)
That's what I get when using "summary(quoll_f, terse = T)"
1 2 3 4 5 6 7 8 9
Occasions 7 7 7 7 7 7 4 4 4
Detections 4 15 13 6 3 1 2 2 5
Animals 2 6 8 4 1 1 1 1 2
Detectors 48 150 150 150 150 150 200 200 200
And "m.array(quoll_f)"
R 2 3 4 5 6 7 8 9 NRecap
1 2 0 0 0 0 0 0 0 0 2
2 6 3 0 0 0 0 0 0 3
3 8 3 0 0 0 0 0 5
4 4 0 0 0 0 0 4
5 1 0 0 0 0 1
6 0 0 0 0 0
7 1 0 0 1
8 1 1 0
9 2 2
I've tried using "JSSAsecrlCL" and leaving D out of
the model formula (then, request D as a derived variable afterwards). The results I'm getting don't seem right, but there's something else that I should probably worry about first:
quoll_model1<- openCR.fit(quoll_f, type = 'JSSAsecrlCL', mask=mask_quoll_f)
Warning message:
In openCR.fit(quoll_f, type = "JSSAsecrlCL", mask = mask_quoll_f) :
multi-session mask provided; using first
> quoll_model1
openCR.fit( capthist = quoll_f, type = JSSAsecrlCL, mask = mask_quoll_f )
openCR 2.2.1, 14:44:54 24 Nov 2021
elapsed time 1.142 minutes
N animals : 19
N detections : 51
N sessions : 9 (secondary 54)
Intervals : 0 0 0 0 0 0 3 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0
Analysis type : JSSAsecrlCL
Model : lambda0~1 phi~1 lambda~1 sigma~1
Fixed (real) : none
Movement model : static
N parameters : 4
Log likelihood : -436.4813
AIC : 880.9627
AICc : 883.8198
Parameter Link
lambda0 log
phi logit
lambda log
sigma log
Beta parameters (coefficients)
beta SE.beta lcl ucl
lambda0 -7.392795 0.1753763 -7.7365263 -7.049064
phi 1.376652 0.2813675 0.8251815 1.928122
lambda -1.222904 0.0000000 -1.2229044 -1.222904
sigma 59.895997 0.0000000 59.8959965 59.895997
Eigenvalues : 1 0.38807 0 0
Numerical rank of Hessian : 2 ( svtol = 1e-05 )
Warning: at least one real parameter is not identifiable
Variance-covariance matrix of beta parameters
lambda0 phi lambda sigma
lambda0 0.030756852 0.001089842 0 0
phi 0.001089842 0.079167670 0 0
lambda 0.000000000 0.000000000 0 0
sigma 0.000000000 0.000000000 0 0
Fitted (real) parameters evaluated at base levels of covariates
lambda0
session estimate SE.estimate lcl ucl
1 0.0006156727 0.0001079744 0.0004365855 0.0008682215
2 0.0006156727 0.0001079744 0.0004365855 0.0008682215
3 0.0006156727 0.0001079744 0.0004365855 0.0008682215
4 0.0006156727 0.0001079744 0.0004365855 0.0008682215
5 0.0006156727 0.0001079744 0.0004365855 0.0008682215
6 0.0006156727 0.0001079744 0.0004365855 0.0008682215
7 0.0006156727 0.0001079744 0.0004365855 0.0008682215
8 0.0006156727 0.0001079744 0.0004365855 0.0008682215
9 0.0006156727 0.0001079744 0.0004365855 0.0008682215
phi
session estimate SE.estimate lcl ucl
1 0.7984527 0.04527934 0.6953351 0.8730414
2 0.7984527 0.04527934 0.6953351 0.8730414
3 0.7984527 0.04527934 0.6953351 0.8730414
4 0.7984527 0.04527934 0.6953351 0.8730414
5 0.7984527 0.04527934 0.6953351 0.8730414
6 0.7984527 0.04527934 0.6953351 0.8730414
7 0.7984527 0.04527934 0.6953351 0.8730414
8 0.7984527 0.04527934 0.6953351 0.8730414
9 NA NA NA NA
lambda
session estimate SE.estimate lcl ucl
1 0.2943739 0 0.2943739 0.2943739
2 0.2943739 0 0.2943739 0.2943739
3 0.2943739 0 0.2943739 0.2943739
4 0.2943739 0 0.2943739 0.2943739
5 0.2943739 0 0.2943739 0.2943739
6 0.2943739 0 0.2943739 0.2943739
7 0.2943739 0 0.2943739 0.2943739
8 0.2943739 0 0.2943739 0.2943739
9 NA NA NA NA
sigma
session estimate SE.estimate lcl ucl
1 1.029202e+26 0 1.029202e+26 1.029202e+26
2 1.029202e+26 0 1.029202e+26 1.029202e+26
3 1.029202e+26 0 1.029202e+26 1.029202e+26
4 1.029202e+26 0 1.029202e+26 1.029202e+26
5 1.029202e+26 0 1.029202e+26 1.029202e+26
6 1.029202e+26 0 1.029202e+26 1.029202e+26
7 1.029202e+26 0 1.029202e+26 1.029202e+26
8 1.029202e+26 0 1.029202e+26 1.029202e+26
9 1.029202e+26 0 1.029202e+26 1.029202e+26
> derived(quoll_model1)
Total number observed 19
Parameters in model lambda0, phi, lambda, sigma
Superpopulation density 0.06320149 per ha
Session-specific counts and estimates:
stratum session n R m r z time lambda0 phi lambda sigma b f gamma D
1 1 2 2 0 0 0 0 0.0006157 0.7985 NaN 1.029e+26 2.007e+00 NaN NA 1.268e-01
1 2 6 6 0 3 0 3 0.0006157 0.7985 0.2944 1.029e+26 -9.704e-01 -0.5041 NaN 3.236e-03
1 3 8 8 3 3 0 4 0.0006157 0.7985 0.2944 1.029e+26 -2.581e-02 -0.5041 2.712 9.525e-04
1 4 4 4 3 0 0 5 0.0006157 0.7985 0.2944 1.029e+26 -7.597e-03 -0.5041 2.712 2.804e-04
1 5 1 1 0 0 0 6 0.0006157 0.7985 0.2944 1.029e+26 -2.236e-03 -0.5041 2.712 8.254e-05
1 6 1 0 0 0 0 7 0.0006157 0.7985 0.2944 1.029e+26 -6.583e-04 -0.5041 2.712 2.430e-05
1 7 1 1 0 0 0 8 0.0006157 0.7985 0.2944 1.029e+26 -1.938e-04 -0.5041 2.712 7.153e-06
1 8 1 1 0 1 0 9 0.0006157 0.7985 0.2944 1.029e+26 -5.705e-05 -0.5041 2.712 2.106e-06
1 9 2 2 1 0 0 10 0.0006157 NA NA 1.029e+26 -1.679e-05 NA 2.712 6.198e-07
Any thoughts on this?
Thanks for any help you can provide,
Gabrielle
Murray Efford
Nov 24, 2021, 12:05:06 PM11/24/21
to Gabrielle Beca, secr
1. You do not have enough data, I'm afraid
2. You might squeeze something out of what you have with a multi-session analysis in 'secr' (not 'openCR'), and some heroic assumptions (detection parameters and density constant over time or fixed at 'known' values etc.). But this is really not what the methods were designed for.3. The mask warning from openCR flags that you have provided multiple masks when openCR uses just one.
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