SMCwithModPossionTL: Sequential Monte Carlo algorithm

In vnminin/bayessir: Particle marginal Metropolis-Hastings (PMMH) algorithm for time-varying Susceptible-Infectious-Recovered-Susceptible (SIRS) model

Description

Sequential Monte Carlo algorithm

Usage

SMCwithModPossionTL(observedCounts, observedTimes, parameters, alphaBreaks,
  startMeans, nparticles, popSize, reportingRate, deltatval, ncrit, UseGill,
  UseSIWR)

Arguments

observedCounts	Integer vector of observed counts
observedTimes	Numeric vector of observation times
parameters	Numeric vector of size seven containing the current values of the infectious contact rate, the recovery rate, the rate at which immunity is lost, and the powers
alphaBreaks	List containing the time-varying environmental force of infection and the times at which the rate changes, broken up by simulation intervals
startMeans	Numeric vector of size two containing the means of initial distributions for the numbers of susceptible and infected individuals respectively
nparticles	Number of particles
popSize	Population size
reportingRate	Probability of infected individuals seeking treatment
deltatval	Initial value to use for tau in tau-leaping algorithm
ncrit	Critical size for modified tau-leaping algorithm; if the population of a compartment is lower than this number a single step algorithm is used until the population gets above the critical size.
UseGill	boolian; if 1, uses the gillespie algorithm. If 0, uses tau-leaping algorithm

Value

log-likelihood and estimate of last state

vnminin/bayessir documentation built on May 3, 2019, 6:17 p.m.