Skip to contents

Geweke-Brooks plot for objects of class mtar

Source: R/wrapperfunctions.R
geweke_plotTAR.Rd

This function is a wrapper around geweke.plot() that applies the Geweke-Brooks convergence diagnostic to the MCMC chains obtained from a fitted mtar model.

Usage

geweke_plotTAR(
  x,
  frac1 = 0.1,
  frac2 = 0.5,
  nbins = 20,
  pvalue = 0.05,
  auto.layout = TRUE,
  ask,
  ...
)

Arguments

x

An object of class mtar returned by a call to mtar().

frac1

fraction to use from beginning of chain

frac2

fraction to use from end of chain

nbins

Number of segments

pvalue

p-value used to plot confidence limits for the null hypothesis

auto.layout

If TRUE then, set up own layout for plots, otherwise use existing one

ask

If TRUE then prompt user before displaying each page of plots. Default is dev.interactive().

...

Additional graphical parameters passed to the plotting routines.

See also

geweke.plot

Examples

# \donttest{
###### Example 1: Returns of the closing prices of three financial indexes
data(returns)
fit1 <- mtar(~ COLCAP + BOVESPA | SP500, data=returns, row.names=Date,
             subset={Date<="2015-12-07"}, dist="Student-t",
             ars=ars(nregim=3,p=c(1,1,2)), n.burnin=100, n.sim=200,
             n.thin=2)
geweke_plotTAR(fit1)
#> 
#> Thresholds
#> 
#> Autoregressive coefficients Regime 1 
#> 
#> Scale parameter Regime 1 
#> 
#> Autoregressive coefficients Regime 2 
#> 
#> Scale parameter Regime 2 
#> 
#> Autoregressive coefficients Regime 3 
#> 
#> Scale parameter Regime 3 
#> 
#> Extra parameter

###### Example 2: Rainfall and two river flows in Colombia
data(riverflows)
fit2 <- mtar(~ Bedon + LaPlata | Rainfall, data=riverflows, row.names=Date,
             subset={Date<="2009-02-13"}, dist="Laplace",
             ars=ars(nregim=3,p=5), n.burnin=100, n.sim=200, n.thin=2)
geweke_plotTAR(fit2)
#> 
#> Thresholds
#> 
#> Autoregressive coefficients Regime 1 
#> 
#> Scale parameter Regime 1 
#> 
#> Autoregressive coefficients Regime 2 
#> 
#> Scale parameter Regime 2 
#> 
#> Autoregressive coefficients Regime 3 
#> 
#> Scale parameter Regime 3 

###### Example 3: Temperature, precipitation, and two river flows in Iceland
data(iceland.rf)
fit3 <- mtar(~ Jokulsa + Vatnsdalsa | Temperature | Precipitation,
             data=iceland.rf, subset={Date<="1974-11-06"}, row.names=Date,
             ars=ars(nregim=2,p=15,q=4,d=2), n.burnin=100, n.sim=200,
             n.thin=2, dist="Slash")
geweke_plotTAR(fit3)
#> 
#> Thresholds
#> 
#> Autoregressive coefficients Regime 1 
#> 
#> Scale parameter Regime 1 
#> 
#> Autoregressive coefficients Regime 2 
#> 
#> Scale parameter Regime 2 
#> 
#> Extra parameter

###### Example 4: U.S. stock returns
data(US.returns)
fit4 <- mtar(~ CCR | dVIX, data=US.returns, subset={Date<="2025-11-28"},
             row.names=Date, ars=ars(nregim=2,p=3,d=3), n.burnin=100,
             n.sim=200, n.thin=2, dist="Student-t")
geweke_plotTAR(fit4)
#> 
#> Thresholds
#> 
#> Autoregressive coefficients Regime 1 
#> 
#> Scale parameter Regime 1 
#> 
#> Autoregressive coefficients Regime 2 
#> 
#> Scale parameter Regime 2 
#> 
#> Extra parameter

# }