move.HMM.state_probs: Compute conditional and posterior state probabilities

In benaug/move.HMM: Fit HMM and HSMM animal movement models

Description

This function, modified from Zucchini and MacDonald (2009), computes the conditional state probabilities and posterior state probabilities (Patterson et al. 2009) using the stationary distribution as the initial distribution. It takes as input a move.HMM object.

Usage

  move.HMM.state_probs(move.HMM)

Arguments

move.HMM

A move.HMM object containing a fitted HMM model.

Value

A list of conditional and posterior state probabilities. #'

Examples

## Not run: 
#2 states, 2 dist-lognorm, wrapped normal
lmean=c(-3,-1) #meanlog parameters
sd=c(1,1) #sdlog parameters
rho<-c(0.2,0.3) # wrapped normal concentration parameters
mu<-c(pi,0) # wrapped normal mean parameters
gamma0=matrix(c(0.6,0.4,0.2,0.8),byrow=T,nrow=2)

dists=c("lognormal","wrpnorm")
nstates=2
turn=c(1,2)
params=vector("list",3)
params[[1]]=gamma0
params[[2]]=cbind(lmean,sd)
params[[3]]=cbind(mu,rho)
obs=move.HMM.simulate(dists,params,1000)
turn=c(1,2)
move.HMM=move.HMM.mle(obs,dists,params,stepm=35,iterlim=100,turn=turn)
#get conditional and posterior state probabilities
move.HMM.state_probs(move.HMM)

## End(Not run)

benaug/move.HMM documentation built on Jan. 23, 2022, 4:29 a.m.