| plot_comp {mbsts} | R Documentation |
Plot Posterior State Components
Description
Plots of the mean of posterior state components of each target series, which is generated by the model training procedure of the MBSTS model.
Usage
plot_comp(
object,
slope,
local,
season,
cyc,
time = NULL,
title = NULL,
component_selection = "All"
)
## S4 method for signature 'mbsts'
plot_comp(
object,
slope,
local,
season,
cyc,
time = NULL,
title = NULL,
component_selection = "All"
)
Arguments
object |
An object of the mbsts class created by a call to the mbsts_function function. |
slope |
A logical vector indicating whether there is trend for each target series, such as c(T,T). |
local |
A logical vector indicating whether there is local level for each target series, such as c(T,T). |
season |
A numerical vector indicating the seasonality for each target series, such as c(12,0). |
cyc |
A logical vector indicating whether there is a cycle component for each target series, such as c(F,T). |
time |
Null or a data frame for time index of the time series. The default value is data.frame(seq(1,n)). |
title |
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",...). |
component_selection |
A character variable whose value must be one of "All", "Trend", "Seasonal", "Cycle", and "Regression". Here, "Trend" means the trend component only and "All" means all the components. |
Author(s)
Jinwen Qiu qjwsnow_ctw@hotmail.com Ning Ning patricianing@gmail.com
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.