hmodel argument to msm. The initial values
for the parameters of the distribution should be given as arguments.hmmCat(prob, basecat)
hmmIdent(x)
hmmUnif(lower, upper)
hmmNorm(mean, sd)
hmmLNorm(meanlog, sdlog)
hmmExp(rate)
hmmGamma(shape, rate)
hmmWeibull(shape, scale)
hmmPois(rate)
hmmBinom(size, prob)
hmmTNorm(mean, sd, lower, upper)
hmmMETNorm(mean, sd, lower, upper, sderr, meanerr=0)
hmmMEUnif(lower, upper, sderr, meanerr=0)hmmCat) Vector of probabilities of observing
category 1, 2, ..., length(prob) respectively.hmmCat) Category which is considered to be the "baseline",
so that during estimation, the probabilities are parameterised as
probabilities relative to this baseline category. By default, the
category with the greatest probabilityhmmIdent) Code in the data which denotes the exactly-observed state.hmmNorm,hmmLNorm,hmmTNorm) Mean defining a Normal, or truncated Normal
distribution.hmmNorm,hmmLNorm,hmmTNorm) Standard deviation defining a
Normal, or truncated Normal distribution.hmmNorm,hmmLNorm,hmmTNorm) Mean on the log
scale, for a log Normal distribution.hmmNorm,hmmLNorm,hmmTNorm) Standard deviation on
the log scale, for a log Normal distribution.hmmGamma) Shape parameter of a Gamma
distribution (see dgamma).dbinom).hmmUnif,hmmTNorm,hmmMEUnif) Lower limit for an Uniform or truncated Normal distribution.hmmUnif,hmmTNorm,hmmMEUnif) Upper limit for an Uniform or truncated Normal
distribution.hmmMETNorm,hmmUnif) Standard deviation of the Normal measurement error
distribution.hmmMETNorm,hmmUnif) Additional shift in the
measurement error, fixed to 0 by default. This may
be modelled in terms of covariates.hmodel, which is a
list containing information about the model. The only component
which may be useful to end users is r, a function of one
argument n which returns a random sample of size n from
the given distribution.Parameters which can be modelled in terms of covariates, on the scale of a link function, are as follows.
mean identity
meanlog identity
rate log
scale log
meanerr identity
prob logit
}
Parameters basecat, lower, upper, size, meanerr are fixed at
their initial values. All other parameters are estimated while fitting
the hidden Markov model, unless the appropriate fixedpars
argument is supplied to msm.
For categorical response distributions (hmmCat) the
outcome probabilities initialized to zero are fixed at zero, and the
probability corresponding to basecat is fixed to one minus the
sum of the remaining probabilities. These remaining probabilities are
estimated, and can be modelled in terms of covariates.
Jackson, C.H., Sharples, L.D., Thompson, S.G. and Duffy, S.W. and Couto, E. Multi-state Markov models for disease progression with classification error. The Statistician, 52(2): 193--209 (2003).
msm