exdqlmChecks: exDQLM Diagnostics

In exdqlm: Extended Dynamic Quantile Linear Models

exDQLM Diagnostics

Description

The function computes the following for the model(s) provided: the posterior predictive loss criterion based off the check loss, the one-step-ahead distribution sequence and its KL divergence from normality. The function also plots the following: the qq-plot and ACF plot corresponding to the one-step-ahead distribution sequence, and a time series plot of the MAP standard forecast errors.

Usage

exdqlmChecks(
  y,
  m1,
  m2 = NULL,
  plot = TRUE,
  cols = c("grey", "grey"),
  ref = NULL
)

Arguments

y	A univariate time-series.
m1	An object of class "exdqlm".
m2	An optional additional object of class "exdqlm" to compare with m1.
plot	If TRUE, the following will be plotted for m1 and m2 (if provided): a qq-plot and ACF plot of the MAP one-step-ahead distribution sequence, and a time series plot of the standardized forecast errors.
cols	Color(s) used to plot diagnostics.
ref	Reference sample of size length(y) from a standard normal distribution used to compute the KL divergence.

Value

A list containing the following is returned:

• m1.uts - The one-step-ahead distribution sequence of m1.

• m1.KL - The KL divergence of m1.uts and a standard normal.

• m1.pplc - The posterior predictive loss criterion of m1 based off the check loss function.

• m1.qq - The ordered pairs of the qq-plot comparing m1.uts with a standard normal distribution.

• m1.acf - The autocorrelations of m1.uts by lag.

If m2 is provided, analogous results for m2 are also included in the list.

Examples

y = scIVTmag[1:100]
model = polytrendMod(1,mean(y),10)
M0 = exdqlmISVB(y,p0=0.85,model,df=c(0.95),dim.df = c(1),
                  gam.init=-3.5,sig.init=15)
check.out = exdqlmChecks(y,M0,plot=FALSE)
check.out$m1.KL
check.out$m1

exdqlm documentation built on Feb. 16, 2023, 7:29 p.m.