covmat: Estimate a covariance matrix from algorithm traces

In pomp: Statistical Inference for Partially Observed Markov Processes

Estimate a covariance matrix from algorithm traces

Description

A helper function to extract a covariance matrix.

Usage

## S4 method for signature 'pmcmcd_pomp'
covmat(object, start = 1, thin = 1, expand = 2.38, ...)

## S4 method for signature 'pmcmcList'
covmat(object, start = 1, thin = 1, expand = 2.38, ...)

## S4 method for signature 'abcd_pomp'
covmat(object, start = 1, thin = 1, expand = 2.38, ...)

## S4 method for signature 'abcList'
covmat(object, start = 1, thin = 1, expand = 2.38, ...)

## S4 method for signature 'probed_pomp'
covmat(object, ...)

Arguments

object	an object extending ‘pomp’
start	the first iteration number to be used in estimating the covariance matrix. Setting thin > 1 allows for a burn-in period.
thin	factor by which the chains are to be thinned
expand	the expansion factor
...	ignored

Value

When object is the result of a pmcmc or abc computation, covmat(object) gives the covariance matrix of the chains. This can be useful, for example, in tuning the proposal distribution.

When object is a ‘probed_pomp’ object (i.e., the result of a probe computation), covmat(object) returns the covariance matrix of the probes, as applied to simulated data.

See Also

MCMC proposals.

Other extraction methods: coef(), cond_logLik(), eff_sample_size(), filter_mean(), filter_traj(), forecast(), logLik, obs(), pred_mean(), pred_var(), saved_states(), spy(), states(), summary(), time(), timezero(), traces()

pomp documentation built on June 8, 2025, 10:20 a.m.