plot_cvg {mbsts}

Plot for Convergence Diagnosis

Description

Plot of the parameter draw for MCMC iterations after burn-in.

Usage

plot_cvg(
  object,
  index,
  type = "o",
  col = "blue",
  pch = 16,
  lty = 1,
  xlab = "Number of iterations",
  ylab = "Estimation",
  main = "Predictor",
  cex.axis = 1.15
)

## S4 method for signature 'mbsts'
plot_cvg(
  object,
  index,
  type = "o",
  col = "blue",
  pch = 16,
  lty = 1,
  xlab = "Number of iterations",
  ylab = "Estimation",
  main = "Predictor",
  cex.axis = 1.15
)

Arguments

object

An object of the mbsts class created by a call to the mbsts_function function.

index

A numerical value indicating which predictor to analyze. The index can be generated by a call to the para.est function

type

NULL or a character vector whose entries are titles for the plots of target series' posterior state components, such as c("Posterior State Components of y1", "Posterior State Components of y2"). The default is c("y1","y2",...).

col

The same setting as that of the plot function in the base package.

pch

The same setting as that of the plot function in the base package.

lty

The same setting as that of the plot function in the base package.

xlab

The same setting as that of the plot function in the base package.

ylab

The same setting as that of the plot function in the base package.

main

The same setting as that of the plot function in the base package.

cex.axis

The same setting as that of the plot function in the base package.

Author(s)

Ning Ning [e-mail]

References

Qiu, Jammalamadaka and Ning (2018), Multivariate Bayesian Structural Time Series Model, Journal of Machine Learning Research 19.68: 1-33.

Ning and Qiu (2021), The mbsts package: Multivariate Bayesian Structural Time Series Models in R.

Jammalamadaka, Qiu and Ning (2019), Predicting a Stock Portfolio with the Multivariate Bayesian Structural Time Series Model: Do News or Emotions Matter?, International Journal of Artificial Intelligence, Vol. 17, Number 2.