hmmpars.boot: Bootstrap availability HMM.

In david-borchers/hmltm: Line transect estimation with hidden Markov availability models

Bootstrap availability HMM.

Description

Bootstraps hidden Markov model (HMM) data from multiple observed availability time series (one per animal).

Usage

hmmpars.boot(availhmm, adat, animals, seed = NULL, B, printprog = TRUE)

Arguments

availhmm	list with availability HMM paramters, as for the hmm.pars parameter of est.hmltm.
adat	list of availability data time series. The ith element of the list must be named $ai and must be a vector of 0s and 1s, with 0 corresponding to being unavailable and 1 to being available.
animals	a vector of integers indicating which of the elements of adat are to be resampled.
seed	random number seed (integer).
B	number of bootstraps to perform (integer).
printprog	if TRUE prints progress through animals as it resamples.

Details

Simulates a new series of availability observations (0s and 1s) using functions dthmm and simulate from library HiddenMarkov, then fits a HMM to these data using function BaumWelch from library HiddenMarkov. Constructs a new hmm.pars object from the fitted HMM parameters.

Does the above B times, each time reformtting the hmm.pars object as a vector using vectorize.hmmpars and then entering this as the next row in a matrix of dimension BxT, where T=3+length(animals)*(nstate^2+nstate*2) and nstate is the number of states in the HMM.

Value

A matrix of dimension BxT, where T is as described above.

Examples

data(bowhead.hmm.pars)
data(bowhead.adat)
animals=1:8
bs=hmmpars.boot(bowhead.hmm.pars,bowhead.adat,animals,B=3)

david-borchers/hmltm documentation built on Oct. 29, 2023, 9:07 p.m.