ARCensReg (version 3.0.1)

residuals: Extract model residuals from ARpCRM or ARtpCRM objects

Description

The conditional residuals are obtained by subtracting the fitted values from the response vector, while the quantile residuals are obtained by inverting the estimated distribution function for each observation to obtain approximately normally distributed residuals. See, for instance, dunn1996randomized;textualARCensReg and kalliovirta2012misspecification;textualARCensReg.

Usage

# S3 method for ARpCRM
residuals(object, ...)
# S3 method for ARtpCRM
residuals(object, ...)

Value

An object of class "residARpCRM", with the following components:

residuals

Vector with the conditional residuals of length \(n\).

quantile.resid

Vector with the quantile residuals of length \(n\).

Generic function plot has methods to show a graphic of residual vs. time, an autocorrelation plot, a histogram, and Quantile-Quantile (Q-Q) plot for the quantile residuals.

Arguments

object

An object inheriting from class ARpCRM or ARtpCRM, representing a fitted AR(p) censored linear model.

...

Further arguments passed to or from other methods.

Author

Fernanda L. Schumacher, Katherine L. Valeriano, Victor H. Lachos, Christian E. Galarza, and Larissa A. Matos

References

dunn1996randomizedARCensReg

kalliovirta2012misspecificationARCensReg

See Also

ARCensReg, ARtCensReg

Examples

# \donttest{
## Example 1: Generating data with normal innovations
set.seed(93899)
x = cbind(1, runif(300))
dat1 = rARCens(n=300, beta=c(1,-1), phi=c(.48,-.2), sig2=.5, x=x, 
              cens='left', pcens=.05, innov="norm")

# Fitting the model with normal innovations
mod1 = ARCensReg(dat1$data$cc, dat1$data$lcl, dat1$data$ucl, dat1$data$y, 
                 x, p=2, tol=0.001)
mod1$tab
plot(residuals(mod1))

# Fitting the model with Student-t innovations
mod2 = ARtCensReg(dat1$data$cc, dat1$data$lcl, dat1$data$ucl, dat1$data$y, 
                  x, p=2, tol=0.001)
mod2$tab
plot(residuals(mod2))


## Example 2: Generating heavy-tailed data
set.seed(12341)
x = cbind(1, runif(300))
dat2 = rARCens(n=300, beta=c(1,-1), phi=c(.48,-.2), sig2=.5, x=x, 
              cens='left', pcens=.05, innov="t", nu=3)

# Fitting the model with normal innovations
mod3 = ARCensReg(dat2$data$cc, dat2$data$lcl, dat2$data$ucl, dat2$data$y,
                 x, p=2, tol=0.001)
mod3$tab
plot(residuals(mod3))

# Fitting the model with Student-t innovations
mod4 = ARtCensReg(dat2$data$cc, dat2$data$lcl, dat2$data$ucl, dat2$data$y,
                  x, p=2, tol=0.001)
mod4$tab
plot(residuals(mod4))
# }