# Testing for Pooled Petersen

#### Carl James Schwarz

#### 2025-02-05

# 1 Testing if a Pooled Petersen is appropriate

It is often of interest to know if a simple Pooled Petersen estimator, i.e., complete pooling over rows and columns, is appropriate.

As noted in Schwarz and Taylor (1998), the Pooled Petersen is unbiased under many conditions, but the most common are:

- Homogeneity of tagging probabilities, i.e. the probability of a fish being tagged in the release stratum is equal across all release strata.
- Homogeneity of recapture probabilities, i.e. the probability of a fish being recaptured is equal across all recovery strata.
- Complete Mixing, i.e. tagged fish mix completely with untagged fish.
- Correlation between tagging and recovery probabilities is zero, i.e. while probabilities are heterogeneous across fish, the two events are statistically independent.

We can examine the first of these conditions by examining the results of the stratified analysis and the results of a (logical) row pooling over all release strata.

# 2 Fitting the models

## 2.1 Reading in the data

This data were made available from the Canadian Department of Fisheries and Oceans and represent release and recaptured of female fish in the Lower Shuswap region.

    test.data.csv <- textConnection("
      86   ,     54   ,     39   ,   219
      76   ,     35   ,     45   ,   168
      24   ,     53   ,     73   ,   190
    1039   ,   1148   ,   2009   ,   0")

    test.data <- as.matrix(read.csv(test.data.csv, header=FALSE, strip.white=TRUE))
    test.data
    #>        V1   V2   V3  V4
    #> [1,]   86   54   39 219
    #> [2,]   76   35   45 168
    #> [3,]   24   53   73 190
    #> [4,] 1039 1148 2009   0

We now fit several models

- A fully 3x3 stratified analysis
- Pooling over all rows using logical pooling
- Pooling over all rows using physical pooling
- Pooling over all rows and the last two columns using physical pooling
- Complete physical pooling over rows and columns (a classical Pooled Petersen)

## 2.2 Fully 3x3 stratified analysis

    library(SPAS)
    mod1 <- SPAS.fit.model(test.data,
                           model.id="No restrictions",
                           row.pool.in=1:3, col.pool.in=1:3)
    #> Using nlminb to find conditional MLE
    #> outer mgc:  1407.445
    #> outer mgc:  4133.626
    #> outer mgc:  1602.162
    #> outer mgc:  237.747
    #> outer mgc:  11.2797
    #> outer mgc:  49.32575
    #> outer mgc:  4.749331
    #> outer mgc:  2.049442
    #> outer mgc:  9.45052
    #> outer mgc:  2.380802
    #> outer mgc:  10.40597
    #> outer mgc:  2.647875
    #> outer mgc:  10.87054
    #> outer mgc:  1.960908
    #> outer mgc:  27.7445
    #> outer mgc:  5.406239
    #> outer mgc:  7.31579
    #> outer mgc:  4.551982
    #> outer mgc:  2.173492
    #> outer mgc:  1.405963
    #> outer mgc:  8.919649
    #> outer mgc:  0.7700033
    #> outer mgc:  3.907549
    #> outer mgc:  1.077837
    #> outer mgc:  1.705958
    #> outer mgc:  0.9025297
    #> outer mgc:  0.5799697
    #> outer mgc:  0.2992383
    #> outer mgc:  0.1649127
    #> outer mgc:  0.08758061
    #> outer mgc:  0.04651695
    #> outer mgc:  0.02470727
    #> outer mgc:  0.01312329
    #> outer mgc:  0.006970476
    #> outer mgc:  0.003702399
    #> outer mgc:  0.001966548
    #> outer mgc:  0.001044543
    #> outer mgc:  0.0005548146
    #> Convergence codes from nlminb  0 relative convergence (4)
    #> Finding conditional estimate of N

    SPAS.print.model(mod1)
    #> Model Name: No restrictions
    #>    Date of Fit: 2025-02-05 23:06
    #>    Version of OPEN SPAS used : SPAS-R 2025.2.1
    #>
    #> Raw data
    #>        V1   V2   V3  V4
    #> [1,]   86   54   39 219
    #> [2,]   76   35   45 168
    #> [3,]   24   53   73 190
    #> [4,] 1039 1148 2009   0
    #>
    #> Row pooling setup : 1 2 3
    #> Col pooling setup : 1 2 3
    #> Physical pooling  : TRUE
    #> Theta pooling     : FALSE
    #> CJS pooling       : FALSE
    #>
    #>
    #> Chapman estimator of population size  10240  (SE  324  )
    #>
    #>
    #> Raw data AFTER PHYSICAL (but not logical) POOLING
    #>       pool1 pool2 pool3  V4
    #> pool1    86    54    39 219
    #> pool2    76    35    45 168
    #> pool3    24    53    73 190
    #>        1039  1148  2009   0
    #>
    #> Condition number of XX' where X= (physically) pooled matrix is  123.8103
    #> Condition number of XX' after logical pooling                   123.8103
    #>
    #> Large value of kappa (>1000) indicate that rows are approximately proportional which is not good
    #>
    #>   Conditional   Log-Likelihood: 30329.53    ;  np: 15 ;  AICc: -60629.07
    #>
    #>   Code/Message from optimization is:  0 relative convergence (4)
    #>
    #> Estimates
    #>               pool1  pool2  pool3 psi cap.prob exp factor Pop Est
    #> pool1          86.0   54.0   39.0 219    1.000        0.0     398
    #> pool2          76.5   33.0   46.5 168    0.127        6.9    2558
    #> pool3          24.5   44.7   80.7 190    0.046       20.8    7413
    #> est unmarked 1038.0 1158.0 2000.0   0       NA         NA   10369
    #>
    #> SE of above estimates
    #>              pool1 pool2 pool3  psi cap.prob exp factor Pop Est
    #> pool1          9.3   7.3   6.2 14.8    0.000        0.0       0
    #> pool2          8.7   5.4   7.0 13.0    0.035        2.2     711
    #> pool3          5.0   4.3   7.5 13.8    0.006        2.8     953
    #> est unmarked    NA    NA    NA  0.0       NA         NA     467
    #>
    #>
    #> Chisquare gof cutoff  : 0.1
    #> Chisquare gof value   : 2.581688
    #> Chisquare gof df      : 0
    #> Chisquare gof p       : NA

#3 Pooling over all rows using logical pooling

    mod2 <- SPAS.fit.model(test.data,
                               model.id="Logical pooling to single row",
                               row.pool.in=c(1,1,1), col.pool.in=1:3, row.physical.pool=FALSE)
    #> Using nlminb to find conditional MLE
    #> outer mgc:  3908.402
    #> outer mgc:  3442.019
    #> outer mgc:  2339.248
    #> outer mgc:  1225.938
    #> outer mgc:  141.9137
    #> outer mgc:  72.98649
    #> outer mgc:  10.65131
    #> outer mgc:  2.106192
    #> outer mgc:  0.01326909
    #> outer mgc:  2.850842e-06
    #> Convergence codes from nlminb  0 relative convergence (4)
    #> Finding conditional estimate of N

    SPAS.print.model(mod2)
    #> Model Name: Logical pooling to single row
    #>    Date of Fit: 2025-02-05 23:06
    #>    Version of OPEN SPAS used : SPAS-R 2025.2.1
    #>
    #> Raw data
    #>        V1   V2   V3  V4
    #> [1,]   86   54   39 219
    #> [2,]   76   35   45 168
    #> [3,]   24   53   73 190
    #> [4,] 1039 1148 2009   0
    #>
    #> Row pooling setup : 1 1 1
    #> Col pooling setup : 1 2 3
    #> Physical pooling  : FALSE
    #> Theta pooling     : FALSE
    #> CJS pooling       : FALSE
    #>
    #>
    #> Chapman estimator of population size  10240  (SE  324  )
    #>
    #>
    #> Raw data AFTER PHYSICAL (but not logical) POOLING
    #>        pool1 pool2 pool3  V4
    #> pool.1    86    54    39 219
    #> pool.1    76    35    45 168
    #> pool.1    24    53    73 190
    #>         1039  1148  2009   0
    #>
    #> Condition number of XX' where X= (physically) pooled matrix is  123.8103
    #> 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: 30304.4    ;  np: 13 ;  AICc: -60582.8
    #>
    #>   Code/Message from optimization is:  0 relative convergence (4)
    #>
    #> Estimates
    #>               pool1  pool2  pool3 psi cap.prob exp factor Pop Est
    #> pool.1         58.7   50.8   55.7 219    0.104        8.7    3841
    #> pool.1         51.9   32.9   64.3 168    0.104        8.7    3127
    #> pool.1         16.4   49.9  104.3 190    0.104        8.7    3282
    #> est unmarked 1098.0 1156.0 1942.0   0       NA         NA   10250
    #>
    #> SE of above estimates
    #>              pool1 pool2 pool3  psi cap.prob exp factor Pop Est
    #> pool.1         5.5   6.0   8.2 14.8    0.004        0.4     165
    #> pool.1         5.3   5.1   8.7 13.0    0.004        0.4     134
    #> pool.1         3.2   6.0  10.2 13.8    0.004        0.4     141
    #> est unmarked    NA    NA    NA  0.0       NA         NA     325
    #>
    #>
    #> Chisquare gof cutoff  : 0.1
    #> Chisquare gof value   : 53.85277
    #> Chisquare gof df      : 2
    #> Chisquare gof p       : 2.023111e-12

## 2.3 Pooling over all rows using physical pooling

    mod3 <- SPAS.fit.model(test.data,
                               model.id="Physical pooling to single row",
                               row.pool.in=c(1,1,1), col.pool.in=1:3)
    #> Using nlminb to find conditional MLE
    #> outer mgc:  4138.626
    #> outer mgc:  1519.635
    #> outer mgc:  267.9392
    #> outer mgc:  25.4021
    #> outer mgc:  0.7073742
    #> outer mgc:  0.0009756013
    #> outer mgc:  2.069271e-09
    #> Convergence codes from nlminb  0 relative convergence (4)
    #> Finding conditional estimate of N
    SPAS.print.model(mod3)
    #> Model Name: Physical pooling to single row
    #>    Date of Fit: 2025-02-05 23:06
    #>    Version of OPEN SPAS used : SPAS-R 2025.2.1
    #>
    #> Raw data
    #>        V1   V2   V3  V4
    #> [1,]   86   54   39 219
    #> [2,]   76   35   45 168
    #> [3,]   24   53   73 190
    #> [4,] 1039 1148 2009   0
    #>
    #> Row pooling setup : 1 1 1
    #> Col pooling setup : 1 2 3
    #> Physical pooling  : TRUE
    #> Theta pooling     : FALSE
    #> CJS pooling       : FALSE
    #>
    #>
    #> Chapman estimator of population size  10240  (SE  324  )
    #>
    #>
    #> Raw data AFTER PHYSICAL (but not logical) POOLING
    #>   pool1 pool2 pool3  V4
    #> 1   186   142   157 577
    #>    1039  1148  2009   0
    #>
    #> Condition number of XX' where X= (physically) pooled matrix is  1
    #> 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: 31438.32    ;  np: 5 ;  AICc: -62866.64
    #>
    #>   Code/Message from optimization is:  0 relative convergence (4)
    #>
    #> Estimates
    #>               pool1  pool2  pool3 psi cap.prob exp factor Pop Est
    #> 1             126.9  133.7  224.4 577    0.104        8.7   10250
    #> est unmarked 1098.0 1156.0 1942.0   0       NA         NA   10250
    #>
    #> SE of above estimates
    #>              pool1 pool2 pool3 psi cap.prob exp factor Pop Est
    #> 1              6.6   6.8  10.8  24    0.004        0.4     325
    #> est unmarked    NA    NA    NA   0       NA         NA     325
    #>
    #>
    #> Chisquare gof cutoff  : 0.1
    #> Chisquare gof value   : 53.85277
    #> Chisquare gof df      : 2
    #> Chisquare gof p       : 2.023111e-12

## 2.4 Pooling over all rows and last two columns using physical pooling

    # do physical complete pooling
    mod4 <- SPAS.fit.model(test.data,
                               model.id="Physical pooling all rows and last two colum ns",
                               row.pool.in=c(1,1,1), col.pool.in=c(1,1,3))
    #> Using nlminb to find conditional MLE
    #> outer mgc:  4133.875
    #> outer mgc:  3617.132
    #> outer mgc:  908.9971
    #> outer mgc:  128.982
    #> outer mgc:  5.742086
    #> outer mgc:  0.02915585
    #> outer mgc:  1.360541e-06
    #> Convergence codes from nlminb  0 relative convergence (4)
    #> Finding conditional estimate of N
    SPAS.print.model(mod4)
    #> Model Name: Physical pooling all rows and last two colum ns
    #>    Date of Fit: 2025-02-05 23:06
    #>    Version of OPEN SPAS used : SPAS-R 2025.2.1
    #>
    #> Raw data
    #>        V1   V2   V3  V4
    #> [1,]   86   54   39 219
    #> [2,]   76   35   45 168
    #> [3,]   24   53   73 190
    #> [4,] 1039 1148 2009   0
    #>
    #> Row pooling setup : 1 1 1
    #> Col pooling setup : 1 1 3
    #> Physical pooling  : TRUE
    #> Theta pooling     : FALSE
    #> CJS pooling       : FALSE
    #>
    #>
    #> Chapman estimator of population size  10240  (SE  324  )
    #>
    #>
    #> Raw data AFTER PHYSICAL (but not logical) POOLING
    #>   pool1 pool3  V4
    #> 1   328   157 577
    #>    2187  2009   0
    #>
    #> Condition number of XX' where X= (physically) pooled matrix is  1
    #> 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: 33180.75    ;  np: 4 ;  AICc: -66353.49
    #>
    #>   Code/Message from optimization is:  0 relative convergence (4)
    #>
    #> Estimates
    #>               pool1  pool3 psi cap.prob exp factor Pop Est
    #> 1             260.6  224.4 577    0.104        8.7   10250
    #> est unmarked 2254.0 1942.0   0       NA         NA   10250
    #>
    #> SE of above estimates
    #>              pool1 pool3 psi cap.prob exp factor Pop Est
    #> 1             12.3  10.8  24    0.004        0.4     325
    #> est unmarked    NA    NA   0       NA         NA     325
    #>
    #>
    #> Chisquare gof cutoff  : 0.1
    #> Chisquare gof value   : 42.0552
    #> Chisquare gof df      : 1
    #> Chisquare gof p       : 8.873292e-11

## 2.5 Complete physical pooling (Pooled Petersen Estimator)

    # do physical complete pooling
    mod5 <- SPAS.fit.model(test.data,
                               model.id="Physical complete pooling",
                               row.pool.in=c(1,1,1), col.pool.in=c(1,1,1))
    #> Using nlminb to find conditional MLE
    #> outer mgc:  4139.929
    #> outer mgc:  12199
    #> outer mgc:  3556.981
    #> outer mgc:  739.2359
    #> outer mgc:  92.68885
    #> outer mgc:  5.542151
    #> outer mgc:  0.03100836
    #> outer mgc:  9.911703e-07
    #> Convergence codes from nlminb  0 relative convergence (4)
    #> Finding conditional estimate of N
    SPAS.print.model(mod5)
    #> Model Name: Physical complete pooling
    #>    Date of Fit: 2025-02-05 23:06
    #>    Version of OPEN SPAS used : SPAS-R 2025.2.1
    #>
    #> Raw data
    #>        V1   V2   V3  V4
    #> [1,]   86   54   39 219
    #> [2,]   76   35   45 168
    #> [3,]   24   53   73 190
    #> [4,] 1039 1148 2009   0
    #>
    #> Row pooling setup : 1 1 1
    #> Col pooling setup : 1 1 1
    #> Physical pooling  : TRUE
    #> Theta pooling     : FALSE
    #> CJS pooling       : FALSE
    #>
    #>
    #> Chapman estimator of population size  10240  (SE  324  )
    #>
    #>
    #> Raw data AFTER PHYSICAL (but not logical) POOLING
    #>      1  V4
    #> 1  485 577
    #>   4196   0
    #>
    #> Condition number of XX' where X= (physically) pooled matrix is  1
    #> 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: 36412.34    ;  np: 3 ;  AICc: -72818.69
    #>
    #>   Code/Message from optimization is:  0 relative convergence (4)
    #>
    #> Estimates
    #>                 1 psi cap.prob exp factor Pop Est
    #> 1             485 577    0.104        8.7   10250
    #> est unmarked 4196   0       NA         NA   10250
    #>
    #> SE of above estimates
    #>               1 psi cap.prob exp factor Pop Est
    #> 1            22  24    0.004        0.4     325
    #> est unmarked NA   0       NA         NA     325
    #>
    #>
    #> Chisquare gof cutoff  : 0.1
    #> Chisquare gof value   : 2.025601e-15
    #> Chisquare gof df      : 0
    #> Chisquare gof p       : NA

# 3 Get the model objects fitted by TMB and create a report

    model.list <- mget( ls()[grepl("^mod.$",ls())])
    names(model.list)
    #> [1] "mod1" "mod2" "mod3" "mod4" "mod5"

    report <- plyr::ldply(model.list, function(x){
       #browser()
       data.frame(#version=x$version,
                  date   = as.Date(x$date),
                  model.id         = x$model.info$model.id,
                  s.a.pool         =-1+nrow(x$fit.setup$pooldata),
                  t.p.pool         =-1+ncol(x$fit.setup$pooldata),
                  logL.cond        = x$model.info$logL.cond,
                  np               = x$model.info$np,
                  AICc             = x$model.info$AICc,
                  gof.chisq        = round(x$gof$chisq,1),
                  gof.df           = x$gof$chisq.df,
                  gof.p            = round(x$gof$chisq.p,3),
                  Nhat             = round(x$est$real$N),
                  Nhat.se          = round(x$se $real$N))

    })
    report
    #>    .id       date                                        model.id s.a.pool
    #> 1 mod1 2025-02-06                                 No restrictions        3
    #> 2 mod2 2025-02-06                   Logical pooling to single row        3
    #> 3 mod3 2025-02-06                  Physical pooling to single row        1
    #> 4 mod4 2025-02-06 Physical pooling all rows and last two colum ns        1
    #> 5 mod5 2025-02-06                       Physical complete pooling        1
    #>   t.p.pool logL.cond np      AICc gof.chisq gof.df gof.p  Nhat Nhat.se
    #> 1        3  30329.53 15 -60629.07       2.6      0    NA 10369     467
    #> 2        3  30304.40 13 -60582.80      53.9      2     0 10250     325
    #> 3        3  31438.32  5 -62866.64      53.9      2     0 10250     325
    #> 4        2  33180.75  4 -66353.49      42.1      1     0 10250     325
    #> 5        1  36412.34  3 -72818.69       0.0      0    NA 10250     325

The AIC should be compared ONLY for the first two models because they are based on the same set of data. You cannot compare models that differ in the physical pooling

In this case, there is good evidence that the Pooled Petersen is too coarse because the goodness of fit statistic for the second model is very large (with a corresponding small goodness-of-fit p-value). Similarly, the AIC indicates that the model is 3x3 stratification (first model) is preferable to the model with complete row pooling (second model).

Notice that the estimates of the population size are identical under logical or physical row pooling (models 2 and 3). And how you pool columns (models 3, 4, 5) but assuming that the number of rows (after logical or physical pooling as long the number of rows is not larger than the number of columns) does not affect the population size estimate (or standard error).

# 4 References

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