Epidemic_fsMCMC: Endemic (SIS) fsMCMC

In JMacDonaldPhD/Epidemics: Inference For Epidemics (one line, title case)

Description

Epidemic_fsMCMC MCMC algorithm to make inference on panel data of an epidemic through forward simulation

Usage

Epidemic_fsMCMC(
  N,
  a,
  x,
  beta0,
  gamma0,
  kernel = NULL,
  no_draws,
  s,
  T_obs,
  k,
  lambda,
  V,
  no_its,
  burn_in = 0,
  lag_max = NA,
  thinning_factor = 1
)

Arguments

N	Total size of closed population
x	panel data observed. Follows a random sample of n individuals and observes them at k timepoints
beta0	beta starting value (Infection Rate Parameter)
gamma0	gamma starting value (Removal rate parameter)
no_draws	How many rexp(1) and runif(1) draws to make for use in the Gillespie algorithm.
T_obs	the period for which the epidemic is observed.
k	how many equally spaced observations take place
lambda	RWM proposal parameter
V	RWM proposal covariance matrix
no_its	The number of MCMC iterations
burn_in	How many of the MCMC iterations are thrown away as burn in (Convergence to Stationary Distn)
lag_max	When plotting the estimated ACF of samples, what will be the maximum lag estimated/plotted
thinning_factor	Create Storage Matrix Proposal Acceptance Counter Propose new beta and gamma using Multiplicative RW propsal Draw New Random Variables Store State Calculating Summary Statistics for samples
initial_infective	Number of individuals who are initially infected in the population

JMacDonaldPhD/Epidemics documentation built on Jan. 10, 2020, 2:48 a.m.