plot.residARpCRM {ARCensReg} R Documentation

## 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 class 'residARpCRM'
plot(x, ...)


### Arguments

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

A ggplot object.

### Author(s)

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

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)


[Package ARCensReg version 3.0.1 Index]