ARCensReg (version 3.0.1)

plot.residARpCRM: Show diagnostic residual plots

Description

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.

Usage

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

Value

A ggplot object.

Arguments

x

An object inheriting from class residARpCRM obtained as an output of function residuals.

...

Additional arguments.

Author

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

# \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)
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)
# }