para.est {mbsts}

Regression parameter estimation by the MBSTS Model

Description

Generate feature selection and parameter estimation results of a mbsts object. Provide means and standard deviations of parameter estimation results for selected features.

Usage

para.est(object, prob.threshold = 0.2)

## S4 method for signature 'mbsts'
para.est(object, prob.threshold = 0.2)

Arguments

object

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

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 is 0.2.#' @param 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.

Value

A list with the following components

index

An array of feature selection results.

para.est.mean

An array of means of parameter estimation values of selected features.

para.est.sd

An array of standard deviations of parameter estimation values of selected features.

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.