Calculates estimates of confidence intervals for the parameters of a
model fitted by hmm.discnp
. Uses a method based quantiles
of estimates produced by simulation (or “parametric
bootstrapping”).
squantCI(object, expForm = TRUE, seed = NULL, alpha = 0.05,
nsim=100, verbose = TRUE)
A npar
-by-2 matrix (where npar
is the number
of “independent” parameters in the model) whose rows
form the estimated confidence intervals. (The first entry of
each row is the lower bound of a confidence interval for the
corresponding parameter, and the second entry is the upper bound.
The row labels indicate the parameters to which each row pertains,
in a reasonably perspicuous manner. The column labels indicate
the relevant quantiles in percentages.
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 confidence intervals produced
be for the parameters expressed in “exponential”
(or “smooth” or “logistic”) form?
If expForm=FALSE
then the parameters 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 \(10^5\).
Positive real number strictly between 0 and 1. A set of
100*(1-alpha)
% confidence intervals will be produced.
A positive integer. The number of simulations upon which the confidence interval estimates 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
.
scovmat()
link{rhmm}()
link{hmm)}()
if (FALSE) {
y <- list(lindLandFlows$deciles,ftLiardFlows$deciles)
fit <- hmm(y,K=3)
CIs <- squantCI(fit,nsim=100)
}
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