prepare_HMM | R Documentation |
prepare_HMM
prepare_HMM(data, hmm.model = NULL, diag.var, order.var = diag.var[1])
data |
data |
hmm.model |
hmm.model |
diag.var |
diag.var |
order.var |
order.var |
## Not run: # 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) ## End(Not run)
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