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 Sept. 11, 2025, 9:09 a.m.