momentuHMM (version 1.5.5)

mixtureProbs: Mixture probabilities

Description

For a fitted model, this function computes the probability of each individual being in a particular mixture

Usage

mixtureProbs(m, getCI = FALSE, alpha = 0.95)

Value

The matrix of individual mixture probabilities, with element [i,j] the probability of individual i being in mixture j

Arguments

m

momentuHMM or momentuHierHMM object

getCI

Logical indicating whether to calculate standard errors and logit-transformed confidence intervals for fitted momentuHMM or momentuHierHMM object. Default: FALSE.

alpha

Significance level of the confidence intervals (if getCI=TRUE). Default: 0.95 (i.e. 95% CIs).

Details

When getCI=TRUE, it can take a while for large data sets and/or a large number of mixtures because the model likelihood for each individual must be repeatedly evaluated in order to numerically approximate the SEs.

References

Maruotti, A., and T. Ryden. 2009. A semiparametric approach to hidden Markov models under longitudinal observations. Statistics and Computing 19: 381-393.

Examples

if (FALSE) {
nObs <- 100
nbAnimals <- 20
dist <- list(step="gamma",angle="vm")
Par <- list(step=c(100,1000,50,100),angle=c(0,0,0.1,2))

# create sex covariate
cov <- data.frame(sex=factor(rep(c("F","M"),each=nObs*nbAnimals/2)))
formulaPi <- ~ sex + 0

# Females more likely in mixture 1, males more likely in mixture 2
beta <- list(beta=matrix(c(-1.5,-0.5,-1.5,-3),2,2),
             pi=matrix(c(-2,2),2,1,dimnames=list(c("sexF","sexM"),"mix2"))) 

data.mix<-simData(nbAnimals=nbAnimals,obsPerAnimal=nObs,nbStates=2,dist=dist,Par=Par,
                  beta=beta,formulaPi=formulaPi,mixtures=2,covs=cov) 

Par0 <- list(step=Par$step, angle=Par$angle[3:4])   
m.mix <- fitHMM(data.mix, nbStates=2, dist=dist, Par0 = Par0, 
                beta0=beta,formulaPi=formulaPi,mixtures=2)
                
mixProbs <- mixtureProbs(m.mix, getCI=TRUE)
}