hmmpars.boot: Bootstrap availability HMM.

In DistanceDevelopment/hsltm: Line transect estimation with hidden state availability models

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)

DistanceDevelopment/hsltm documentation built on June 21, 2019, 2:22 p.m.