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MCMC Convergence

Source: R/convergence.R
convergence.Rd

Monitor convergence of the MCMC algorithms.

Usage

convergence(object, param = NULL, type = "trace", print_names = FALSE)

Arguments

object

An object of class estimate or explore

param

Character string. Names of parameters for which to monitor MCMC convergence.

type

Character string. Which type of convergence plot ? The current options are trace (default) and acf.

print_names

Logical. Should the parameter names be printed (defaults to FALSE)? This can be used to first determine the parameter names to specify in type.

Value

A list of ggplot objects.

Note

An overview of MCMC diagnostics can be found here.

Examples

# \donttest{
# note: iter = 250 for demonstrative purposes

# data
Y <- ptsd[,1:5]

#########################
###### continuous #######
#########################
fit <- estimate(Y, iter = 250,
                progress = FALSE)

# print names first
convergence(fit, print_names = TRUE)
#> Error in convergence(fit, print_names = TRUE): 'data' not of class 'mids'

# trace plots
convergence(fit, type = "trace",
            param = c("B1--B2", "B1--B3"))[[1]]
#> Error in convergence(fit, type = "trace", param = c("B1--B2", "B1--B3")): 'data' not of class 'mids'

# acf plots
convergence(fit, type = "acf",
            param = c("B1--B2", "B1--B3"))[[1]]
#> Error in convergence(fit, type = "acf", param = c("B1--B2", "B1--B3")): 'data' not of class 'mids'
# }