Calculate bias correction term for calf:cow composition survey.
Source:R/compositionBiasCorrection.R
compositionBiasCorrection.Rd
When composition surveys are conducted there is a possibility of bias in calf cow ratios due to misidentifying young bulls as adult females and vice versa or missing calves. Here we address this gap with a bias term derived from a simple model of the recruitment survey observation process. See Hughes et al. (2025) Section 2.2 for a detailed description of the model.
Arguments
- w
number. The apparent number of adult females per collared animal in composition survey.
- q
number in 0, 1. Ratio of bulls to cows in composition survey groups.
- u
number in 0, 1. Probability of misidentifying young bulls as adult females and vice versa in composition survey.
- z
number in 0, <1. Probability of missing calves in composition survey.
- approx
logical. If TRUE approximate the uncertainty about the value of the composition bias correction value (c) with the log-normal distribution of c given all the supplied values of
q
,u
, andz
. If FALSE the composition bias correction value (c) is returned for each value ofq
,u
, andz
Value
number or tibble. If approx = FALSE
a vector of composition bias
correction values (c) of the same length as q
, u
, and z
. If approx = TRUE
a tibble with on row per unique value of w
and columns w
, m
,
v
, sig2
, mu
representing w
, mean c
, variance of c
, and parameters for a
log-normal approximation of the distribution of c
.
References
Hughes, J., Endicott, S., Calvert, A.M. and Johnson, C.A., 2025. Integration of national demographic-disturbance relationships and local data can improve caribou population viability projections and inform monitoring decisions. Ecological Informatics, 87, p.103095. https://doi.org/10.1016/j.ecoinf.2025.103095
See also
Caribou demography functions:
bbouMakeSummaryTable()
,
caribouBayesianPM()
,
caribouPopGrowth()
,
caribouPopSimMCMC()
,
demographicCoefficients()
,
demographicProjectionApp()
,
demographicRates()
,
doSim()
,
getBBNationalInformativePriors()
,
getOutputTables()
,
getPriors()
,
getScenarioDefaults()
,
getSimsInitial()
,
getSimsNational()
,
plotRes()
,
popGrowthTableJohnsonECCC
,
runScnSet()
,
simulateObservations()
Examples
# number or reps
nr <- 10
compositionBiasCorrection(w = 6,
q = runif(nr, 0, 0.6),
u = runif(nr, 0, 0.2),
z = runif(nr, 0, 0.2),
approx = FALSE)
#> [1] 1.1457987 1.0944496 0.9024906 1.1677261 0.9948077 1.1239331 0.9433013
#> [8] 1.0637733 1.0133138 0.9791924
compositionBiasCorrection(w = 6,
q = runif(nr, 0, 0.6),
u = runif(nr, 0, 0.2),
z = runif(nr, 0, 0.2),
approx = TRUE)
#> # A tibble: 1 × 5
#> w m v sig2 mu
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 6 1.02 0.00887 0.00841 0.0203