CV: Coefficient of Variation
Description
The coefficient of variation of effective sampling area predicts the
bias in estimated density (Efford and Mowat 2013). 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 2013); 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. (2013) Compensatory heterogeneity in
capture--recapture data.Ecology In press.