point.est.compare: Compare model fits to data sets

In dill/wisp: Wildlife Simulation Package (wisp)

Description

This function will produce a tabulated summary of point estimates from a list of fitted models. They are sorted in order of decreasing AIC score, and AIC weights are computed, so that weighted averages of abundances are also computed.

Usage

point.est.compare(...)

Arguments

...	Wisp objects containing point estimates from a data set. All objects in the list must be of similar types, i.e., estimates from distance sampling estimators, or estimates from capture-recapture estimators.

Details

If all of the included objects do not possess AIC values, then AIC weights and consequently weighted averages will not be produced.

Value

Produces a matrix with a row for each model entered as an argument, and final row of weighted average. Columns include model name, AIC, delta AIC, AIC weight, estimated number of groups, estimated number of individuals, and estimated group size.

Author(s)

Mike Meredith, Wildlife Conservation Society [e-mail]

References

Burnham and Anderson. 2002. Model selection and multimodel inference, second edition. Springer.

See Also

point.est.crM0, point.est.lt

Examples

data(taxi.samp.cr)
my.point.est.crM0 <- point.est.crM0(taxi.samp.cr)
my.point.est.crMt <- point.est.crMt(taxi.samp.cr)
my.point.est.crMb <- point.est.crMb(taxi.samp.cr)
my.point.est.crMh <- point.est.crMh(taxi.samp.cr)
point.est.compare(my.point.est.crM0, my.point.est.crMb, 
                  my.point.est.crMt, my.point.est.crMh)

data(tortoise.samp.lt)
lt.halfnorm <- point.est.lt(tortoise.samp.lt, plot=TRUE, title=TRUE, 
                 conditional=TRUE, model="half.normal")
lt.hazrate <- point.est.lt(tortoise.samp.lt, plot=TRUE, title=TRUE, 
                 conditional=TRUE, model="hazard.rate")

dill/wisp documentation built on May 15, 2019, 8:31 a.m.