Produces an estimate of the covariance matrix of the parameter
estimates in a model fitted by hmm.discnp
. Uses a method
based on simulation (or “parametric bootstrapping”).
scovmat(object, expForm=TRUE, seed = NULL, nsim=100, verbose = TRUE)
A (positive definite) matrix which is an estimate of the
covariance of the parameter estimates from the fitted model
specified by object
. It has row and column labels
which indicate the parameters to which its entries pertain,
in a reasonably perspicuous manner.
This matrix has an attribute seed
(the random number
generation seed that was used) so that the calculations can
be reproduced.
An object of class hmm.discnp
as returned by hmm()
.
Logical scalar. Should the covariance matrix produced
be that of the estimates of the parameters expressed in
“exponential” (or “smooth” or “logistic”)
form? If expForm=FALSE
then the parameter estimates
considered are “raw” probabilities, with redundancies
(last column of tpm
; last row of Rho
) removed.
Integer scalar serving as a seed for the random number generator.
If left NULL
the seed itself is chosen randomly from the
set of integers between 1 and \(10^5\).
A positive integer. The number of simulations upon which the covariance matrix estimate will be based.
Logical scalar; if TRUE
, iteration counts will be
printed out during each of the simulation and model-fitting
stages.
Rolf Turner
r.turner@auckland.ac.nz
This function is currently applicable only to models fitted to
univariate data. If there are predictors in the model,
then only the exponential form of the parameters may be used,
i.e. expForm
must be TRUE
.
squantCI()
link{rhmm}()
link{hmm)}()
if (FALSE) {
y <- list(lindLandFlows$deciles,ftLiardFlows$deciles)
fit <- hmm(y,K=3)
ccc <- scovmat(fit,nsim=100)
}
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