Auxiliary Function for the Positive Bernoulli Family Function with Time Effects
auxposbernoulli.t.RdReturns behavioural effects indicator variables from a capture history matrix.
Arguments
- y
Capture history matrix. Rows are animals, columns are sampling occasions, and values should be 0s and 1s only.
- check.y
Logical, if
TRUEthen some basic checking is performed.- rename, name
If
rename = TRUEthen the behavioural effects indicator are named using the value ofnameas the prefix. IfFALSEthen use the same column names asy.
Details
This function can help fit certain capture–recapture models
(commonly known as \(M_{tb}\) or \(M_{tbh}\)
(no prefix \(h\) means it is an intercept-only model)
in the literature).
See posbernoulli.t for details.
Value
A list with the following components.
- cap.hist1
A matrix the same dimension as
y. In any particular row there are 0s up to the first capture. Then there are 1s thereafter.- cap1
A vector specifying which time occasion the animal was first captured.
- y0i
Number of noncaptures before the first capture.
- yr0i
Number of noncaptures after the first capture.
- yr1i
Number of recaptures after the first capture.
Examples
# Fit a M_tbh model to the deermice data:
(pdata <- aux.posbernoulli.t(with(deermice,
cbind(y1, y2, y3, y4, y5, y6))))
#> $cap.hist1
#> bei1 bei2 bei3 bei4 bei5 bei6
#> [1,] 0 1 1 1 1 1
#> [2,] 0 1 1 1 1 1
#> [3,] 0 1 1 1 1 1
#> [4,] 0 1 1 1 1 1
#> [5,] 0 1 1 1 1 1
#> [6,] 0 1 1 1 1 1
#> [7,] 0 1 1 1 1 1
#> [8,] 0 1 1 1 1 1
#> [9,] 0 1 1 1 1 1
#> [10,] 0 1 1 1 1 1
#> [11,] 0 1 1 1 1 1
#> [12,] 0 1 1 1 1 1
#> [13,] 0 1 1 1 1 1
#> [14,] 0 1 1 1 1 1
#> [15,] 0 1 1 1 1 1
#> [16,] 0 0 1 1 1 1
#> [17,] 0 0 1 1 1 1
#> [18,] 0 0 1 1 1 1
#> [19,] 0 0 1 1 1 1
#> [20,] 0 0 1 1 1 1
#> [21,] 0 0 1 1 1 1
#> [22,] 0 0 1 1 1 1
#> [23,] 0 0 1 1 1 1
#> [24,] 0 0 0 1 1 1
#> [25,] 0 0 0 1 1 1
#> [26,] 0 0 0 1 1 1
#> [27,] 0 0 0 1 1 1
#> [28,] 0 0 0 1 1 1
#> [29,] 0 0 0 1 1 1
#> [30,] 0 0 0 0 1 1
#> [31,] 0 0 0 0 1 1
#> [32,] 0 0 0 0 1 1
#> [33,] 0 0 0 0 0 1
#> [34,] 0 0 0 0 0 1
#> [35,] 0 0 0 0 0 1
#> [36,] 0 0 0 0 0 0
#> [37,] 0 0 0 0 0 0
#> [38,] 0 0 0 0 0 0
#>
#> $cap1
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 5 5 5 6 6 6
#>
#> $y0i
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 4 4 4 5 5 5
#>
#> $yr0i
#> [1] 0 2 2 1 0 1 1 2 0 1 1 1 0 2 4 3 2 3 2 2 2 3 2 3 0 1 0 2 3 2 0 1 1 1 1 0 0 0
#>
#> $yr1i
#> [1] 5 3 3 4 5 4 4 3 5 4 4 4 5 3 1 1 2 1 2 2 2 1 2 0 3 2 3 1 0 0 2 1 0 0 0 0 0 0
#>
deermice <- data.frame(deermice,
bei = 0, # Add this
pdata$cap.hist1) # Incorporate these
head(deermice) # Augmented with behavioural effect indicator variables
#> y1 y2 y3 y4 y5 y6 sex age weight bei bei1 bei2 bei3 bei4 bei5 bei6
#> 1 1 1 1 1 1 1 0 y 12 0 0 1 1 1 1 1
#> 2 1 0 0 1 1 1 1 y 15 0 0 1 1 1 1 1
#> 3 1 1 0 0 1 1 0 y 15 0 0 1 1 1 1 1
#> 4 1 1 0 1 1 1 0 y 15 0 0 1 1 1 1 1
#> 5 1 1 1 1 1 1 0 y 13 0 0 1 1 1 1 1
#> 6 1 1 0 1 1 1 0 a 21 0 0 1 1 1 1 1
tail(deermice)
#> y1 y2 y3 y4 y5 y6 sex age weight bei bei1 bei2 bei3 bei4 bei5 bei6
#> 33 0 0 0 0 1 0 0 y 14 0 0 0 0 0 0 1
#> 34 0 0 0 0 1 0 1 y 11 0 0 0 0 0 0 1
#> 35 0 0 0 0 1 0 0 a 24 0 0 0 0 0 0 1
#> 36 0 0 0 0 0 1 0 y 9 0 0 0 0 0 0 0
#> 37 0 0 0 0 0 1 0 a 16 0 0 0 0 0 0 0
#> 38 0 0 0 0 0 1 1 a 19 0 0 0 0 0 0 0