Cross-correlation of MCMC samples — cross

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)
#>             (Intercept)          C1          C2         B21     sigma_y
#> (Intercept)  1.00000000 -0.99932344  0.01671787  0.11588899 -0.04553272
#> C1          -0.99932344  1.00000000 -0.02108018 -0.15109733  0.04676991
#> C2           0.01671787 -0.02108018  1.00000000  0.17958905  0.03570433
#> B21          0.11588899 -0.15109733  0.17958905  1.00000000 -0.03596415
#> sigma_y     -0.04553272  0.04676991  0.03570433 -0.03596415  1.00000000
cross_corr_plot(fit, type = "coda")