autoplot.regime_model: regime_model object ggplot for the outputs on the function...

In adrincont/mtar: Bayesian Approach for MTAR Models with Missing Data

Description

Produces a ggplot object for the results of the mtarns and mtarstr functions.

Usage

## S3 method for class 'regime_model'
autoplot(object, type = 1,...)

Arguments

object	Object of class “regime_model”. Not NULL
type	character string giving the type of plot to be computed. Allowed values are 1 for "Thresholds value chains" (the default), 2 for “Sigma chains”, 3 for “Theta chains”, 4 for “Gamma chains” (2,3 and 4 for each regime and associated with the corresponding parameters) or 5 for “Output process fit”
...	other arguments passed to specific methods

Details

Graph the strings for the outputs corresponding to the functions “mtarns” and “mtarstr” which return an object of class “regime_model”. The chains corresponding to the samplings in each case do not contain the burning period.

Value

Return a ggplot object.

Author(s)

Valeria Bejarano [e-mail] & Andrey Rincon [e-mail]

References

Calderon, S. and Nieto, F. (2017) Bayesian analysis of multivariate threshold autoregress models with missing data. Communications in Statistics - Theory and Methods 46 (1):296–318. doi:10.1080/03610926.2014.990758.

See Also

mtarns, mtarstr

Examples

library(ggplot2)
data("datasim")
data = datasim$Sim$Zt
parameters = list(l = 1,orders = list(pj = 1))
initial = mtarinipars(tsregime_obj = tsregime(data),
                      list_model = list(pars = parameters))
estim1 = mtarns(ini_obj = initial,niter = 500,chain = TRUE)

autoplot.regime_model(estim1,2)
autoplot.regime_model(estim1,3)

autoplot.regime_model(estim1,5)

adrincont/mtar documentation built on Jan. 18, 2022, 9:49 a.m.