Description Usage Arguments Details Value References Examples
Fit an hidden Markov model to the data provided, using numerical optimization of the loglikelihood function.
1 2 3 4 5 
data 
An object 
nbStates 
Number of states of the HMM. 
stepPar0 
Vector of initial statedependent step length distribution parameters.
The parameters should be in the order expected by the pdf of 
anglePar0 
Vector of initial statedependent turning angle distribution parameters.
The parameters should be in the order expected by the pdf of 
beta0 
Initial matrix of regression coefficients for the transition probabilities (more
information in "Details").
Default: 
delta0 
Initial value for the initial distribution of the HMM. Default: 
formula 
Regression formula for the covariates. Default: 
stepDist 
Name of the distribution of the step lengths (as a character string). Supported distributions are: gamma, weibull, lnorm, exp. Default: gamma. 
angleDist 
Name of the distribution of the turning angles (as a character string).
Supported distributions are: vm, wrpcauchy. Set to 
angleMean 
Vector of means of turning angles if not estimated (one for each state).
Default: 
stationary 

knownStates 
Vector of values of the state process which are known prior to fitting the model (if any). Default: NULL (states are not known). This should be a vector with length the number of rows of 'data'; each element should either be an integer (the value of the known states) or NA if the state is not known. 
verbose 
Determines the print level of the optimizer. The default value of 0 means that no printing occurs, a value of 1 means that the first and last iterations of the optimization are detailed, and a value of 2 means that each iteration of the optimization is detailed. 
nlmPar 
List of parameters to pass to the optimization function 
fit 

The matrix beta
of regression coefficients for the transition probabilities has
one row for the intercept, plus one row for each covariate, and one column for
each nondiagonal element of the transition probability matrix. For example, in a 3state
HMM with 2 covariates, the matrix beta
has three rows (intercept + two covariates)
and six columns (six nondiagonal elements in the 3x3 transition probability matrix 
filled in rowwise).
In a covariatefree model (default), beta
has one row, for the intercept.
The choice of initial parameters is crucial to fit a model. The algorithm might not find the global optimum of the likelihood function if the initial parameters are poorly chosen.
A moveHMM
object, i.e. a list of:
mle 
The maximum likelihood estimates of the parameters of the model (if the numerical algorithm
has indeed identified the global maximum of the likelihood function), which is a list
of: 
data 
The movement data 
stepDist 
The step length distribution name 
angleDist 
The turning angle distribution name 
mod 
The object returned by the numerical optimizer 
conditions 
A few conditions used to fit the model ( 
rawCovs 
Raw covariate values, as found in the data (if any). Used in 
knownStates 
Vector of states known a priori, as provided in input (if any, 
.
Patterson T.A., Basson M., Bravington M.V., Gunn J.S. 2009. Classifying movement behaviour in relation to environmental conditions using hidden Markov models. Journal of Animal Ecology, 78 (6), 11131123.
Langrock R., King R., Matthiopoulos J., Thomas L., Fortin D., Morales J.M. 2012. Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology, 93 (11), 23362342.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  ### 1. simulate data
# define all the arguments of simData
nbAnimals < 2
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 < 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 zeroinflation, so no zeromass included
anglePar0 < kappa0 # the angle mean is not estimated, so only the concentration parameter is needed
formula < ~cov1+cos(cov2)
m < fitHMM(data=data,nbStates=nbStates,stepPar0=stepPar0,anglePar0=anglePar0,formula=formula,
stepDist=stepDist,angleDist=angleDist,angleMean=angleMean)
print(m)

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