cross_corr: Cross-correlation of MCMC samples

In JointAI: Joint Analysis and Imputation of Incomplete Data

Cross-correlation of MCMC samples

Description

These functions compute the cross-correlations of the MCMC samples in an JointAI object via coda::crosscorr() and plot them using either the corrplot package or coda::crosscorr.plot().

Usage

cross_corr(object, outcome = 1L, start = NULL, end = NULL, thin = NULL)

cross_corr_plot(object, outcome = 1L, start = NULL, end = NULL,
  thin = NULL, type = "corrplot")

Arguments

object	an object of class JointAI
outcome	integer; index of the outcome model for which the correlations should be plotted
start	the first iteration of interest (see window.mcmc)
end	the last iteration of interest (see window.mcmc)
thin	thinning interval (integer; see window.mcmc). For example, thin = 1 (default) will keep the MCMC samples from all iterations; thin = 5 would only keep every 5th iteration.
type	character; type of plot to be produced. Either "corrplot" (default) or "coda".

Value

a matrix (or a plot)

Examples

fit <- lm_imp(y ~ C1 + C2 + B2, data = wideDF, n.iter = 200)
cross_corr(fit)
cross_corr_plot(fit, type = "coda")

JointAI documentation built on Jan. 30, 2026, 5:07 p.m.