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Check MCMC Convergence for Spatial Random Effects

check_mcmc.Rd

This function checks the Markov Chain Monte Carlo (MCMC) convergence of spatial random effects for either a RiskMap or RiskMap.pred.re object. It plots the trace plot and autocorrelation function (ACF) for the MCMC chain and calculates the effective sample size (ESS).

Usage

check_mcmc(object, check_mean = TRUE, component = NULL, ...)

Arguments

object

An object of class RiskMap or RiskMap.pred.re. RiskMap is the output from glgpm function, and RiskMap.pred.re is obtained from the pred_over_grid function.

check_mean

Logical. If TRUE, checks the MCMC chain for the mean of the spatial random effects. If FALSE, checks the chain for a specific component of the random effects vector.

component

Integer. The index of the spatial random effects component to check when check_mean = FALSE. Must be a positive integer corresponding to a location in the data. Ignored if check_mean = TRUE.

...

Additional arguments passed to the acf function for customizing the ACF plot.

Value

No return value, called for side effects (plots and warnings).

Details

The function first checks that the input object is either of class RiskMap or RiskMap.pred.re. Depending on the value of check_mean, it either calculates the mean of the spatial random effects across all locations for each iteration or uses the specified component. It then generates two plots: - A trace plot of the selected spatial random effect over iterations. - An autocorrelation plot (ACF) with the effective sample size (ESS) displayed in the title.

The ESS is computed using the ess function, which provides a measure of the effective number of independent samples in the MCMC chain.

If check_mean = TRUE, the component argument is ignored, and a warning is issued. To specify a particular component of the random effects vector, set check_mean = FALSE and provide a valid component value.

Author

Emanuele Giorgi e.giorgi@lancaster.ac.uk