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There are several types of problem that can occur that make the use of SPAS problematic.
If the row in the movement matrix are proportional, then there an infinite number of solutions. Note it is the singularity of the movement matrix that is problematic; the number of tags releases and not recovered (last column of the input data) can be proportional without affecting the fit.
The model may converge to different solutions depending on your set up. There is no automatic “flagging” of this situation and you need to be vigilant. A diagnostic tool is the condition number of \(XX'\) where \(X\) is the movement matrix. If the rows are exactly proportional (or more generally if the rows are colinear) the condition number will be \(+\infty\).
The solution is to pool the row(s) that are proportional.
Here are some examples:
test.data.csv <- textConnection("
160 , 120 , 72 , 82 , 3592
80 , 60 , 36 , 41 , 532
7960 , 9720 , 6264 , 7934 , 0 ")
test.data <- as.matrix(read.csv(test.data.csv, header=FALSE, strip.white=TRUE))
test.data
#> V1 V2 V3 V4 V5
#> [1,] 160 120 72 82 3592
#> [2,] 80 60 36 41 532
#> [3,] 7960 9720 6264 7934 0
mod..1 <- SPAS.fit.model(test.data,
model.id="No restrictions",
row.pool.in=1:2, col.pool.in=1:4)
#> Using nlminb to find conditional MLE
#> outer mgc: 21233.77
#> outer mgc: 31337.35
#> outer mgc: 29620.78
#> outer mgc: 7736.026
#> outer mgc: 1637.496
#> outer mgc: 77.12751
#> outer mgc: 4.863
#> outer mgc: 0.1203054
#> outer mgc: 0.04505754
#> outer mgc: 0.01671065
#> Convergence codes from nlminb 0 relative convergence (4)
#> Finding conditional estimate of N
SPAS.print.model(mod..1)
#> Model Name: No restrictions
#> Date of Fit: 2024-01-25 12:19
#> Version of OPEN SPAS used : SPAS-R 2023-03-31
#>
#> Raw data
#> V1 V2 V3 V4 V5
#> [1,] 160 120 72 82 3592
#> [2,] 80 60 36 41 532
#> [3,] 7960 9720 6264 7934 0
#>
#> Row pooling setup : 1 2
#> Col pooling setup : 1 2 3 4
#> Physical pooling : TRUE
#> Theta pooling : FALSE
#> CJS pooling : FALSE
#>
#>
#> Chapman estimator of population size 238286 (SE 8578 )
#>
#>
#> Raw data AFTER PHYSICAL (but not logical) POOLING
#> pool1 pool2 pool3 pool4 V5
#> pool1 160 120 72 82 3592
#> pool2 80 60 36 41 532
#> 7960 9720 6264 7934 0
#>
#> Condition number of XX' where X= (physically) pooled matrix is Inf
#> Condition number of XX' after logical pooling Inf
#>
#> Large value of kappa (>1000) indicate that rows are approximately proportional which is not good
#>
#> Conditional Log-Likelihood: 285758.5 ; np: 12 ; AICc: -571493
#>
#> Code/Message from optimization is: 0 relative convergence (4)
#>
#> Estimates
#> pool1 pool2 pool3 pool4 psi cap.prob exp factor Pop Est
#> pool1 109.1 132.2 85.1 107.7 3592 0.013 73.5 299742
#> pool2 80.0 60.0 36.0 41.0 532 1.000 0.0 749
#> est unmarked 8011.0 9708.0 6251.0 7908.0 0 NA NA 300491
#>
#> SE of above estimates
#> pool1 pool2 pool3 pool4 psi cap.prob exp factor Pop Est
#> pool1 5.3 6.4 4.2 5.3 59.9 0.001 3.5 14291
#> pool2 8.9 7.7 6.0 6.4 23.1 0.000 0.0 0
#> est unmarked NA NA NA NA 0.0 NA NA 13499
#>
#>
#> Chisquare gof cutoff : 0.1
#> Chisquare gof value : 33.49443
#> Chisquare gof df : 2
#> Chisquare gof p : 5.330621e-08In this case, it appears that the model has converged, but this is ONE of many possible solutions and cannot be relied upon.
The condition number is very large (!) as expected and shown in the above output:
# Compute the condition number of XX'
XX <- test.data[1:2, 1:4] %*% t(test.data[1:2, 1:4])
XX
#> [,1] [,2]
#> [1,] 51908 25954
#> [2,] 25954 12977
cat("\n\nCondition number is\n")
#>
#>
#> Condition number is
kappa(XX)
#> [1] InfWe will either physically or logically pool rows:
mod..2 <- SPAS.fit.model(test.data,
model.id="No restrictions",
row.pool.in=c(1,1), col.pool.in=1:4,
row.physical.pool=FALSE)
#> Using nlminb to find conditional MLE
#> outer mgc: 31861.11
#> outer mgc: 31027.39
#> outer mgc: 27743.45
#> outer mgc: 64891.49
#> outer mgc: 17714.73
#> outer mgc: 2938.93
#> outer mgc: 388.7135
#> outer mgc: 1367.762
#> outer mgc: 58.7506
#> outer mgc: 33.22109
#> outer mgc: 0.2950866
#> outer mgc: 0.001589974
#> Convergence codes from nlminb 0 relative convergence (4)
#> Finding conditional estimate of N
SPAS.print.model(mod..2)
#> Model Name: No restrictions
#> Date of Fit: 2024-01-25 12:19
#> Version of OPEN SPAS used : SPAS-R 2023-03-31
#>
#> Raw data
#> V1 V2 V3 V4 V5
#> [1,] 160 120 72 82 3592
#> [2,] 80 60 36 41 532
#> [3,] 7960 9720 6264 7934 0
#>
#> Row pooling setup : 1 1
#> Col pooling setup : 1 2 3 4
#> Physical pooling : FALSE
#> Theta pooling : FALSE
#> CJS pooling : FALSE
#>
#>
#> Chapman estimator of population size 238286 (SE 8578 )
#>
#>
#> Raw data AFTER PHYSICAL (but not logical) POOLING
#> pool1 pool2 pool3 pool4 V5
#> pool.1 160 120 72 82 3592
#> pool.1 80 60 36 41 532
#> 7960 9720 6264 7934 0
#>
#> Condition number of XX' where X= (physically) pooled matrix is Inf
#> Condition number of XX' after logical pooling 1
#>
#> Large value of kappa (>1000) indicate that rows are approximately proportional which is not good
#>
#> Conditional Log-Likelihood: 285750.9 ; np: 11 ; AICc: -571479.7
#>
#> Code/Message from optimization is: 0 relative convergence (4)
#>
#> Estimates
#> pool1 pool2 pool3 pool4 psi cap.prob exp factor Pop Est
#> pool.1 109.4 132.1 85.0 107.5 3592 0.02 49 201170
#> pool.1 54.7 66.0 42.5 53.7 532 0.02 49 37426
#> est unmarked 8036.0 9702.0 6244.0 7896.0 0 NA NA 238596
#>
#> SE of above estimates
#> pool1 pool2 pool3 pool4 psi cap.prob exp factor Pop Est
#> pool.1 6.7 8.7 6.7 8.1 59.9 0.001 1.9 7805
#> pool.1 5.5 7.4 6.0 7.2 23.1 0.001 1.9 1452
#> est unmarked NA NA NA NA 0.0 NA NA 8603
#>
#>
#> Chisquare gof cutoff : 0.1
#> Chisquare gof value : 49.81373
#> Chisquare gof df : 3
#> Chisquare gof p : 8.753258e-11This has solved the colinearity problem.
Of course with real data, it is highly unlikely that the rows in the recovery matrix will be exactly proportional.
The model may converge to different solutions depending on your set up. There is no automatic “flagging” of this situation and you need to be vigilant. The condition number of \(XX'\) may be useful.
The solution is to pool the row(s) that are proportional.
Here are some examples where the entries in row 2 are modified slightly to make the rows only approximately proportional
test.data.csv <- textConnection("
160 , 120 , 72 , 82 , 3592
75 , 62 , 38 , 35 , 532
7960 , 9720 , 6264 , 7934 , 0 ")
test.data <- as.matrix(read.csv(test.data.csv, header=FALSE, strip.white=TRUE))
test.data
#> V1 V2 V3 V4 V5
#> [1,] 160 120 72 82 3592
#> [2,] 75 62 38 35 532
#> [3,] 7960 9720 6264 7934 0
mod..2 <- SPAS.fit.model(test.data,
model.id="No restrictions",
row.pool.in=1:2, col.pool.in=1:4)
#> Using nlminb to find conditional MLE
#> outer mgc: 21468.58
#> outer mgc: 31261.98
#> outer mgc: 29306.81
#> outer mgc: 8081.517
#> outer mgc: 1632.478
#> outer mgc: 75.28523
#> outer mgc: 4.412961
#> outer mgc: 0.1427219
#> outer mgc: 0.05345935
#> outer mgc: 0.01986673
#> Convergence codes from nlminb 0 relative convergence (4)
#> Finding conditional estimate of N
SPAS.print.model(mod..2)
#> Model Name: No restrictions
#> Date of Fit: 2024-01-25 12:19
#> Version of OPEN SPAS used : SPAS-R 2023-03-31
#>
#> Raw data
#> V1 V2 V3 V4 V5
#> [1,] 160 120 72 82 3592
#> [2,] 75 62 38 35 532
#> [3,] 7960 9720 6264 7934 0
#>
#> Row pooling setup : 1 2
#> Col pooling setup : 1 2 3 4
#> Physical pooling : TRUE
#> Theta pooling : FALSE
#> CJS pooling : FALSE
#>
#>
#> Chapman estimator of population size 240468 (SE 8710 )
#>
#>
#> Raw data AFTER PHYSICAL (but not logical) POOLING
#> pool1 pool2 pool3 pool4 V5
#> pool1 160 120 72 82 3592
#> pool2 75 62 38 35 532
#> 7960 9720 6264 7934 0
#>
#> Condition number of XX' where X= (physically) pooled matrix is 1785.545
#> Condition number of XX' after logical pooling 1785.545
#>
#> Large value of kappa (>1000) indicate that rows are approximately proportional which is not good
#>
#> Conditional Log-Likelihood: 285730.4 ; np: 12 ; AICc: -571436.7
#>
#> Code/Message from optimization is: 0 relative convergence (4)
#>
#> Estimates
#> pool1 pool2 pool3 pool4 psi cap.prob exp factor Pop Est
#> pool1 109.1 132.2 85.1 107.7 3592 0.013 73.5 299742
#> pool2 75.0 62.0 38.0 35.0 532 1.000 0.0 742
#> est unmarked 8011.0 9708.0 6251.0 7908.0 0 NA NA 300484
#>
#> SE of above estimates
#> pool1 pool2 pool3 pool4 psi cap.prob exp factor Pop Est
#> pool1 5.3 6.4 4.2 5.3 59.9 0.001 3.5 14291
#> pool2 8.7 7.9 6.2 5.9 23.1 0.000 0.0 0
#> est unmarked NA NA NA NA 0.0 NA NA 13499
#>
#>
#> Chisquare gof cutoff : 0.1
#> Chisquare gof value : 33.49443
#> Chisquare gof df : 2
#> Chisquare gof p : 5.33061e-08
# Compute the condition number of XX'
XX <- test.data[1:2, 1:4] %*% t(test.data[1:2, 1:4])
XX
#> [,1] [,2]
#> [1,] 51908 25046
#> [2,] 25046 12138
cat("\n\nCondition number is\n")
#>
#>
#> Condition number is
kappa(XX)
#> [1] 1785.545Now there is only one solution, but the estimate is very sensitive to small changes in the data. Notice that the condition number is still very large.
In theory this should have no influence on the fit (see the section on pooling columns).
test.data.csv <- textConnection("
160 , 120 , 72 , 82 , 0, 3592
100 , 45 , 39 , 90 , 0, 532
7960 , 9720 , 6264 , 7934 , 0, 0 ")
test.data <- as.matrix(read.csv(test.data.csv, header=FALSE, strip.white=TRUE))
test.data
#> V1 V2 V3 V4 V5 V6
#> [1,] 160 120 72 82 0 3592
#> [2,] 100 45 39 90 0 532
#> [3,] 7960 9720 6264 7934 0 0
mod..3 <- SPAS.fit.model(test.data,
model.id="No restrictions",
row.pool.in=1:2, col.pool.in=1:5)
#> Using nlminb to find conditional MLE
#> outer mgc: 19793.78
#> outer mgc: 31172.76
#> outer mgc: 29040.42
#> outer mgc: 25712.46
#> outer mgc: 5362.637
#> outer mgc: 496.3737
#> outer mgc: 8.392982
#> outer mgc: 0.2236606
#> outer mgc: 0.1492354
#> outer mgc: 0.05639175
#> outer mgc: 0.02111307
#> outer mgc: 0.007818906
#> outer mgc: 0.002723444
#> outer mgc: 0.0009406725
#> Convergence codes from nlminb 0 relative convergence (4)
#> Finding conditional estimate of N
SPAS.print.model(mod..3)
#> Model Name: No restrictions
#> Date of Fit: 2024-01-25 12:19
#> Version of OPEN SPAS used : SPAS-R 2023-03-31
#>
#> Raw data
#> V1 V2 V3 V4 V5 V6
#> [1,] 160 120 72 82 0 3592
#> [2,] 100 45 39 90 0 532
#> [3,] 7960 9720 6264 7934 0 0
#>
#> Row pooling setup : 1 2
#> Col pooling setup : 1 2 3 4 5
#> Physical pooling : TRUE
#> Theta pooling : FALSE
#> CJS pooling : FALSE
#>
#>
#> Chapman estimator of population size 222133 (SE 7617 )
#>
#>
#> Raw data AFTER PHYSICAL (but not logical) POOLING
#> pool1 pool2 pool3 pool4 pool5 V6
#> pool1 160 120 72 82 0 3592
#> pool2 100 45 39 90 0 532
#> 7960 9720 6264 7934 0 0
#>
#> Condition number of XX' where X= (physically) pooled matrix is 46.8607
#> Condition number of XX' after logical pooling 46.8607
#>
#> Large value of kappa (>1000) indicate that rows are approximately proportional which is not good
#>
#> Conditional Log-Likelihood: 286003.7 ; np: 14 ; AICc: -571979.3
#>
#> Code/Message from optimization is: 0 relative convergence (4)
#>
#> Estimates
#> pool1 pool2 pool3 pool4 pool5 psi cap.prob exp factor Pop Est
#> pool1 109.1 132.2 85.1 107.7 0 3592 0.013 73.5 299742
#> pool2 100.0 45.0 39.0 90.0 0 532 1.000 0.0 806
#> est unmarked 8011.0 9708.0 6251.0 7908.0 0 0 NA NA 300548
#>
#> SE of above estimates
#> pool1 pool2 pool3 pool4 pool5 psi cap.prob exp factor Pop Est
#> pool1 5.3 6.4 4.2 5.3 0 59.9 0.001 3.5 14291
#> pool2 10.0 6.7 6.2 9.5 0 23.1 0.000 0.0 0
#> est unmarked NA NA NA NA NA 0.0 NA NA 13499
#>
#>
#> Chisquare gof cutoff : 0.1
#> Chisquare gof value : 33.49436
#> Chisquare gof df : 3
#> Chisquare gof p : 2.533076e-07
XX <- test.data[1:2, 1:5] %*% t(test.data[1:2, 1:5])
XX
#> [,1] [,2]
#> [1,] 51908 31588
#> [2,] 31588 21646
cat("\n\nCondition number is\n")
#>
#>
#> Condition number is
kappa(XX)
#> [1] 46.8607Notice that the column of 0’s does not affect the fit and has no impact on the condition number of \(XX'\).
Darroch, J. N. (1961). The two-sample capture-recapture census when tagging and sampling are stratified. Biometrika, 48, 241–260. https://www.jstor.org/stable/2332748
Plante, N., L.-P Rivest, and G. Tremblay. (1988). Stratified Capture-Recapture Estimation of the Size of a Closed Population. Biometrics 54, 47-60. https://www.jstor.org/stable/2533994
Schwarz, C. J., & Taylor, C. G. (1998). The use of the stratified-Petersen estimator in fisheries management with an illustration of estimating the number of pink salmon (Oncorhynchus gorbuscha) that return to spawn in the Fraser River. Canadian Journal of Fisheries and Aquatic Sciences, 55, 281–296. https://doi.org/10.1139/f97-238
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.