plot_prob {mbsts}

Plot Inclusion Probabilities

Description

Plots of the empirical inclusion probabilities for predictors of each target series, based on a user-defined threshold probability. For example, one predictor is selected 100 times in 200 MCMC draws (after discard burn-in draws), the empirical inclusion probability for that predictor is 0.5. If the user-defined threshold probability less than or equal to 0.5, then this predictor will show in the plot.

Usage

plot_prob(object, title = NULL, prob.threshold = 0.2, varnames = NULL)

## S4 method for signature 'mbsts'
plot_prob(object, title = NULL, prob.threshold = 0.2, varnames = NULL)

Arguments

object

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

title

NULL or A character vector whose entries are titles for the inclusion probability plots generated for each target series, such as c("Inclusion Probabilities for y1", "Inclusion Probabilities for y2"). If Null, the output is c("y1","y2",...).

prob.threshold

A numerical value used as the threshold to only include predictors whose inclusion probabilities are higher than it in the plot. The default value is 0.2.

varnames

NULL or A character vector whose entries are the variable names for predictors, such as c("x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8"). If Null, the output is c("x11","x12",...,"x21","x22",...).

Author(s)

Jinwen Qiu [e-mail] 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.