MisRepARMA-internal: Internal MisRepARMA functions

In MisRepARMA: Misreported Time Series Analysis

Internal MisRepARMA functions

Description

Internal MisRepARMA functions not intended to be called directly by the user.

Usage

estimate(data, tol, p_AR, q_MA, covars = NULL, misReport = "U")
estimate_bayes(data, p_AR, q_MA, covars = NULL, misReport = "U",
               n_chains = 3, n_iter = 2000, n_burnin = 500, n_thin = 1,
               q_prior_a = NULL, q_prior_b = NULL,
               w_prior_a = 1, w_prior_b = 1,
               q_init = NULL, w_init = NULL)
ran.genf(data, n, ran.args)
## S3 method for class 'fitMisRepARMA'
summary(object, ...)
## S3 method for class 'fitMisRepARMA'
print.summary(x, ...)

Arguments

data	numeric vector of observed time series values.
tol	convergence tolerance for the EM algorithm.
p_AR	order of the AR part.
q_MA	order of the MA part.
covars	optional matrix of covariates.
misReport	"U" for underreporting, "O" for overreporting.
n_chains	number of MCMC chains.
n_iter	total MCMC iterations per chain.
n_burnin	burn-in iterations to discard.
n_thin	thinning interval.
q_prior_a	first shape parameter of a Beta prior for q (currently unused; reserved for future use). Default NULL.
q_prior_b	second shape parameter of a Beta prior for q. Default NULL.
w_prior_a	first shape parameter of the Beta prior for \omega: \omega \sim \mathrm{Beta}(a, b). Default 1 (uniform prior).
w_prior_b	second shape parameter of the Beta prior for \omega. Default 1.
q_init	optional starting value for q (MCMC initialisation).
w_init	optional starting value for \omega (MCMC initialisation).
n	length of the simulated series (for ran.genf).
ran.args	list of simulation arguments (for ran.genf).
object	object of class fitMisRepARMA.
x	object of class summary.fitMisRepARMA.
...	further arguments (currently unused).

Details

estimate implements the frequentist iterative EM algorithm.

estimate_bayes implements Bayesian MCMC estimation via JAGS, called internally by fitMisRepARMA when method = "bayes".

ran.genf generates synthetic replicates for the bootstrap.

summary.fitMisRepARMA and print.summary.fitMisRepARMA provide formatted summaries for both frequentist and Bayesian fits.

See Also

MisRepARMA-package, fitMisRepARMA, reconstruct

MisRepARMA documentation built on June 7, 2026, 5:06 p.m.