plot.residARpCRM function

Show diagnostic residual plots

It returns four plots for the quantile residuals: the time series plot of the residuals, the quantile-quantile plot, the histogram, and the ACF plot of the residuals.

## S3 method for class 'residARpCRM'
plot(x, ...)

Arguments

• x: An object inheriting from class residARpCRM obtained as an output of function residuals.
• ...: Additional arguments.

Returns

A ggplot object.

Author(s)

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

See Also

ggplot, ARCensReg, ARtCensReg, residuals.ARpCRM, residuals.ARtpCRM

Examples

## 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)
r1 = residuals(mod1)
class(r1)
plot(r1)

# 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)
r2 = residuals(mod2)
plot(r2)

## 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)
r3 = residuals(mod3)
plot(r3)

# 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)
r4 = residuals(mod4)
plot(r4)