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, seed = NULL, alpha = 0.05, nsim=100, verbose = TRUE)An object of class hmm.discnp as returned by hmm().
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.
A 2-by-npar matrix (where npar is the number of
“independent” parameters in the model) whose columns
form the estimated confidence intervals. The column labels
indicate the parameters to which each column pertains, in a
reasonably perspicuous manner. The row 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.
This function is currently applicable only to models fitted to
univariate data. The confidence intervals calculated are for the
“raw” parameters (entries of tpm with the
last column dropped --- since the rows sum to 1, and the
entries of Rho with the last row dropped --- since
the columns sum to 1.
scovmat() link{rhmm}() link{hmm)}()
# NOT RUN {
y <- list(lindLandFlows$deciles,ftLiardFlows$deciles)
fit <- hmm(y,K=3)
CIs <- squantCI(fit,nsim=100)
# }
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