particleFilter: Run a particle filter for fitmodel object

In sbfnk/fitR: Tool box for fitting dynamic infectious disease models to time series

Run a particle filter for fitmodel object

Description

The particle filter returns an estimate of the marginal log-likelihood L = p(y(t_{1:T})|\theta) as well as the set of filtered trajectories and their respective weights at the last observation time \omega(t_T)=p(y(t_T)|\theta).

Usage

particleFilter(fitmodel, theta, initState, data, nParticles, progress = FALSE)

Arguments

fitmodel	a fitmodel object
theta	named numeric vector. Values of the parameters. Names should match fitmodel$thetaNames.
initState	named numeric vector. Initial values of the state variables. Names should match fitmodel$stateNames.
data	data frame. Observation times and observed data. The time column must be named "time" and the observation column must be named "obs".
nParticles	number of particles
progress	if TRUE progression of the filter is displayed in the console.

Value

A list of 3 elements:

• dPointObs the marginal log-likelihood of the theta.

• traj a list of size nParticles with all filtered trajectories.

• trajWeight a vector of size nParticles with the normalised weight of the filtered trajectories.

Note

An unbiased state sample x(t_{0:T}) ~ p(X(t_{0:T})|\theta,y(t_{0:T})) can be obtained by sampling the set of trajectories traj with probability trajWeight.

See Also

plotSMC

sbfnk/fitR documentation built on July 18, 2023, 3:28 p.m.