prepare_HMM
prepare_HMM(data, hmm.model = NULL, diag.var, order.var = diag.var[1])data
hmm.model
diag.var
order.var
if (FALSE) {
# Example taken from moveHMM package.
# 1. simulate data
# define all the arguments of simData
nbAnimals <- 1
nbStates <- 2
nbCovs <- 2
mu<-c(15,50)
sigma<-c(10,20)
angleMean <- c(pi,0)
kappa <- c(0.7,1.5)
stepPar <- c(mu,sigma)
anglePar <- c(angleMean,kappa)
stepDist <- "gamma"
angleDist <- "vm"
zeroInflation <- FALSE
obsPerAnimal <- c(50,100)
data <- moveHMM::simData(nbAnimals=nbAnimals,nbStates=nbStates,
stepDist=stepDist,angleDist=angleDist,
stepPar=stepPar,anglePar=anglePar,nbCovs=nbCovs,
zeroInflation=zeroInflation,
obsPerAnimal=obsPerAnimal)
### 2. fit the model to the simulated data
# define initial values for the parameters
mu0 <- c(20,70)
sigma0 <- c(10,30)
kappa0 <- c(1,1)
stepPar0 <- c(mu0,sigma0) # no zero-inflation, so no zero-mass included
anglePar0 <- kappa0 # the angle mean is not estimated,
# so only the concentration parameter is needed
formula <- ~cov1+cos(cov2)
m <- moveHMM::fitHMM(data=data,nbStates=nbStates,stepPar0=stepPar0,
anglePar0=anglePar0,formula=formula,
stepDist=stepDist,angleDist=angleDist,angleMean=angleMean)
### 3. Transform into a segmentation-class object
res.hmm <- prepare_HMM(data=data,
hmm.model = m, diag.var = c("step","angle"))
### 4. you can now apply the same function than for segclust2d outputs
plot(res.hmm)
segmap(res.hmm)
}