Description Usage Arguments Details Value Note References See Also Examples
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 
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.,

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 singledetector 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.
CVa
and CVa0
do not work for models with individual
covariates.
Numeric
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.
1 2  ## housemouse model
CVa0(morning.h2 )

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