diagnostic_mtar: Residual diagnosis for model MTAR
In BMTAR: Bayesian Approach for MTAR Models with Missing Data

Description Usage Arguments Details Value Author(s) References Examples

Tests to help evaluate some assumptions about the MTAR model. calculating some tests and graphs.

1	diagnostic_mtar(regime_model,lagmax = NULL,alpha = '0.05')

`regime_model`	Object of class “`regime_model`”. Not NULL
`lagmax`	maximum lag at which to calculate the acf and pacf. Default NULL
`alpha`	level of significance for the graphs, should take values in c('0.10','0.05','0.025','0.01','0.005'). Default '0.05'

For the graphical tests it returns: “Residuals plot” and “Residuals density plot” (overlaps a standard normal density),“Residuals plot” and “Residuals plot”, “CUSUM” statistic for residuals, “ACF” and “PACF” plots for residuals series.

Returns a list of ggplot objects with the graphics mentioned before.

Valeria Bejarano vbejaranos@unal.edu.co, Sergio Calderon sacalderonv@unal.edu.co & Andrey Rincon adrincont@unal.edu.co

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.

library(ggplot2)
data("datasim")
data = datasim$Sim$Z
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,burn = 500)
diagnostic_mtar(estim1)