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.
1 2 3
vector of numeric values
vector of class probabilities
logical; if TRUE missing values are dropped from x
fitted secr finite mixture model
integer sequence number of session to analyse
other arguments passed to predict.secr (e.g.,
CV computes the coefficient of variation of
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 a0 = 2 π
lambda0.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.
CVa0 do not work for models with individual
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 is better avoided.
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.
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