derived: Derived Parameters From openCR Models

In openCR: Open Population Capture-Recapture

Derived Parameters From openCR Models

Description

For ..CL openCR models, compute the superpopulation size or density. For all openCR models, compute the time-specific population size or density from the estimated superpopulation size and the turnover parameters.

Usage

## S3 method for class 'openCR'
derived(object, newdata = NULL, all.levels = FALSE, Dscale = 1, 
    HTbysession = FALSE, ...)
## S3 method for class 'openCRlist'
derived(object, newdata = NULL, all.levels = FALSE, Dscale = 1, 
    HTbysession = FALSE, ...)
openCR.esa(object, bysession = FALSE, stratum = 1)
openCR.pdot(object, bysession = FALSE, stratum = 1)

Arguments

object	fitted openCR model
newdata	optional dataframe of values at which to evaluate model
all.levels	logical; passed to makeNewData if newdata not specified

Dscale	numeric to scale density
HTbysession	logical; Horvitz-Thompson estimates by session (see Details)
...	other arguments (not used)
bysession	logical; if TRUE then esa or pdot is computed separately for each session
stratum	integer

Details

Derived estimates of density and superD are multiplied by Dscale. Use Dscale = 1e4 for animals per 100 sq. km. openCR.esa and openCR.pdot are used internally by derived.openCR.

If HTbysession then a separate H-T estimate is derived for each primary session; otherwise a H-T estimate of the superpopulation is used in combination with turnover parameters (phi, beta) to obtain session-specific estimates. Results are often identical.

The output is an object with its own print method (see print.derivedopenCR).

The code does not yet allow user-specified newdata.

Value

derived returns an object of class c(“derivedopenCR",“list"), list with these components:

totalobserved	number of different individuals detected
parameters	character vector; names of parameters in model (excludes derived parameters)
superN	superpopulation size (non-spatial models only)
superD	superpopulation density (spatial models only)
estimates	data frame of counts and estimates
Dscale	numeric multiplier for printing densities

If newdata has multiple levels then the value is a list of such objects, one for each level.

openCR.pdot returns a vector of experiment-wide detection probabilities under the fitted model (one for each detected animal).

openCR.esa returns a vector of effective sampling areas under the fitted model (one for each detected animal). If 'bysession = TRUE' the result is a list with one component per session.

Note

Prior to 1.4.5, openCR.esa did not expand the result for squeezed capture histories (freq>1) and did not return a list when bysession = TRUE.

See Also

openCR.fit, print.derivedopenCR

Examples

## Not run: 

# override default method to get true ML for L1
L1CL <- openCR.fit(ovenCH, type = 'JSSAlCL', method = 'Nelder-Mead')
predict(L1CL)
derived(L1CL)

## compare to above
L1 <- openCR.fit(ovenCH, type = 'JSSAl', method = 'Nelder-Mead')
predict(L1)
derived(L1)


## End(Not run)

openCR documentation built on Sept. 25, 2022, 5:06 p.m.