converge_diag_param_fn: A function to assess convergence of the posterior sampling of...

In BayesMoFo: Bayesian Mortality Forecasting

A function to assess convergence of the posterior sampling of fitted parameters for monitoring purposes

Description

Produce several convergence diagnostic tools (e.g. trace/density/acf plots and effective sample sizes) from the posterior samples of fitted parameters.

Usage

converge_diag_param_fn(
  result,
  plot_params = NULL,
  trace = TRUE,
  density = TRUE,
  acf_plot = FALSE,
  ESS_all = FALSE
)

Arguments

result	object of type either "fit_result" or "BayesMoFo".
plot_params	A vector of character strings specifying which set of parameters to plot for visualisation. If not specified, a random selection of the parameters will be included in the plots (see fit_result$param or runBayesMoFo_result$result$best$param for a full list) will be chosen. Note that a specific combination of alpha, beta, kappa, and gamma, is to be plotted, then users need to specify the exact indices of them, e.g. plot_params="gamma[1,2]". Otherwise, only three randomly selected of them will be plotted. To see a complete list of parameters, i.e. colnames(fit_result$post_sample[[1]])[!startsWith(colnames(fit_result$post_sample[[1]]),"q[")] or colnames(runBayesMoFo_result$result$best$post_sample[[1]])[!startsWith(colnames(runBayesMoFo_result$result$best$post_sample[[1]]),"q[")].
trace	A logical value to indicate if trace plots of posterior samples of death rates should be shown (default) or suppressed (e.g. to aid visibility).
density	A logical value to indicate if density plots of posterior samples of death rates should be shown (default) or suppressed (e.g. to aid visibility).
acf_plot	A logical value to indicate if auto-correlation plots should be shown or suppressed (default).
ESS_all	A logical value indicating if effective sample sizes are to be computed for all parameters. The default is FALSE where only chosen parameters will be evaluated, if TRUE all parameters will be assessed.

Value

Some convergence-related plots of posterior samples of fitted parameters.

ESS

The effective sample sizes of the chosen parameters.

Examples

#load and prepare data
data("dxt_array_product");data("Ext_array_product")
death<-preparedata_fn(dxt_array_product,strat_name = c("ACI","DB","SCI"),ages=35:65)
expo<-preparedata_fn(Ext_array_product,strat_name = c("ACI","DB","SCI"),ages=35:65)

#fit any mortality model
runBayesMoFo_result<-runBayesMoFo(death=death,expo=expo,models="APCI")

#default plot
converge_runBayesMoFo_result<-converge_diag_param_fn(runBayesMoFo_result)

#ESS
converge_runBayesMoFo_result$ESS

#plot specific parameters 

runBayesMoFo_result$result$best$param  #run this line to check parameters of the model
converge_diag_param_fn(runBayesMoFo_result,plot_params=c("rho","sigma2_kappa","beta")) 
#note only three betas were plotted

colnames(runBayesMoFo_result$result$best$post_sample[[1]])[!startsWith(
  colnames(runBayesMoFo_result$result$best$post_sample[[1]]),"q[")]  
#run the above line to check full list of parameters of the model
converge_diag_param_fn(runBayesMoFo_result,plot_params=c("beta[1,2]","gamma[3,2]")) 

#ACF plot 
converge_diag_param_fn(runBayesMoFo_result,plot_params=c("beta[1,2]","gamma[3,2]"),
trace=FALSE,density=FALSE,acf_plot=TRUE)

BayesMoFo documentation built on Aug. 11, 2025, 1:07 a.m.