CV: Coefficient of Variation

In secr: Spatially Explicit Capture-Recapture

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, ...)

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.

Value

Numeric

Note

Do not confuse the function CVa with the estimated relative standard error of the estimate of a from derived, also labelled CVa in the output. The relative standard error RSE is often labelled CV in the literature on capture–recapture, but this can cause unnecessary confusion. See also RSE.

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

## Not run: 

## 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 )


## End(Not run)

secr documentation built on Nov. 4, 2024, 9:06 a.m.