llik_cov: Evaluate the approximate log-likelihood of an underreported...

In jbracher/hhh4underreporting: Fitting endemic-epidemic models to underreported data

Description

The likelihood approximation is based on an approximation of the process by a second-order equivalent process with complete reporting. Note that this is an R version while a reimplementation in Rcpp is used in the optimization.

Usage

llik_cov(
  Y,
  m1,
  vl1,
  nu,
  phi,
  kappa,
  psi,
  q,
  max_lag = 5,
  return_contributions = FALSE
)

Arguments

Y	a time series of counts (numeric vector)
m1	the initial mean, i.e. $E(lambda_1)$
vl1	the initial variance of lambda, i.e. $Var(lambda_1)$
nu, phi, kappa, psi	the time-varying model parameters (vectors of same length)
psi	overdispersion parameter (scalar)
q	the assumed reporting probability
max_lag	in evaluation of likelihood only lags up to max_lag are taken into account
return_contributions	shall the log-likelihood contributions of each time point be returned (as vector)?

Value

The log-likelihood as scalar or (if return_contributions == TRUE) the vector of log-likelihood contributions.

jbracher/hhh4underreporting documentation built on July 21, 2020, 2:08 p.m.