secr (version 4.6.10)

CV: Coefficient of Variation

Description

The coefficient of variation of effective sampling area predicts the bias in estimated density (Efford and Mowat 2014). These functions assist its calculation from fitted finite mixture models.

Usage

CV(x, p, na.rm = FALSE)
CVa0(object, ...)
CVa(object, sessnum = 1, ...)

Value

Numeric

Arguments

x

vector of numeric values

p

vector of class probabilities

na.rm

logical; if TRUE missing values are dropped from x

object

fitted secr finite mixture model

sessnum

integer sequence number of session to analyse

...

other arguments passed to predict.secr (e.g., newdata)

Details

CV computes the coefficient of variation of x. If p is provided then the distribution is assumed to be discrete, with support x and class membership probabilities p (scaled automatically to sum to 1.0).

CVa computes CV(\(a\)) where \(a\) is the effective sampling area of Borchers and Efford (2008).

CVa0 computes CV(a0) where a0 is the single-detector sampling area defined as \(a_0 = 2 \pi \lambda_0 \sigma^2\) (Efford and Mowat 2014); a0 is a convenient surrogate for a, the effective sampling area. CV(a0) uses either the fitted MLE of a0 (if the a0 parameterization has been used), or a0 computed from the estimates of lambda0 and sigma.

CVa and CVa0 do not work for models with individual covariates.

References

Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum likelihood methods for capture--recapture studies. Biometrics 64, 377--385.

Efford, M. G. and Mowat, G. (2014) Compensatory heterogeneity in capture--recapture data. Ecology 95, 1341--1348.

See Also

CVpdot, derived, details, RSE

Examples

if (FALSE) {

## housemouse model
morning <- subset(housemouse, occ = c(1,3,5,7,9))
msk <- make.mask((traps(morning)), nx = 32) 
morning.h2   <- secr.fit(morning, buffer = 20, model = list(g0~h2), mask = msk, 
    trace = FALSE)
CVa0(morning.h2 )

}