asymptoticCovMat {RHmm}R Documentation

Asymptotic covariance matrix of the HMM parameters

Description

This function calculates the empirical asymptotic covariance matrix of the HMM parameters

Usage

asymptoticCovMat(HMM, obs, asymptMethod=c("nlme", "optim"))

Arguments

HMM a HMMClass or a HMMFitClass object
obs The vector, matrix, data frame, list of vectors or list of matrices of observations
asymptMethod A string which indicates the numerical method for computing the Hessian of parameters. Default 'nlme'.

Value

A matrix

Numerical computations

The Hessian of the LLH function is computed using finite difference approximations. Either the stat package 'optimhess' internal function or the nlme package 'fdHess' function is used for these computations.
There are a lot of numerical difficulties in computing derivatives in such models. 'optimhess' or 'fdHess' could return non inversible Hessian matrix.

References

Visser Ingmar, Raijmakers Maartje E. J. and Molenaar Peter C. M.(2000) Confidence intervals for hidden Markov model parameters, British Journal of Mathematical and Statistical Psychology, 53, 317-327.

Mann Tobias P. (2006) Numerically Stable Hidden Markov Model Implementation, http://bozeman.genome.washington.edu/compbio/mbt599_2006/hmm_scaling_revised.pdf

See Also

HMMFit

Examples

  data(n1d_3s)
  Res_n1d_3s<-HMMFit(obs_n1d_3s, nStates=3)
  covMat <- asymptoticCovMat(Res_n1d_3s, obs_n1d_3s, asymptMethod='optim')

[Package RHmm version 1.2.0 Index]