plot.DiagARpCRM {ARCensReg} R Documentation

## Plot influence diagnostic measures

### Description

Plot method for objects of class "DiagARpCRM".

### Usage

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


### Arguments

 x An object inheriting from class DiagARpCRM. The influence diagnostic measures are calculated by function InfDiag, with three possible perturbation schemes: response perturbation (y), scale matrix perturbation (Sigma), or explanatory variable perturbation (x). ... Additional arguments.

### Value

A ggplot object, plotting the index versus the influence diagnostic measure.

### Author(s)

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

ggplot, InfDiag, ARCensReg

### Examples


library(ggplot2)

# Generating the data
set.seed(12341)
x = cbind(1,runif(100))
dat = rARCens(n=100, beta=c(1,-1), phi=c(.48,-.2), sig2=.5, x=x,
cens='left', pcens=.05)

# Creating an outlier
dat$data$y[40] = 5
ggplot(dat$data) + geom_line(aes(x=1:100, y=y)) + theme_bw() + labs(x="Time") # Fitting the model fit = ARCensReg(dat$data$cc, dat$data$lcl, dat$data$ucl, dat$data$y, x, p=2, tol=0.001, show_se=FALSE) # Influence diagnostic M0y = InfDiag(fit, k=3.5, perturbation="y") plot(M0y) M0Sigma = InfDiag(fit, k=3.5, perturbation="Sigma") plot(M0Sigma) M0x = InfDiag(fit, k=3.5, indcolx=c(0,1), perturbation="x") plot(M0x) # Perturbation on a subset of parameters M0y1 = InfDiag(fit, k=3.5, indpar=c(1,1,0,0,0), perturbation="y")$M0
M0y2 = InfDiag(fit, k=3.5, indpar=c(0,0,1,1,1), perturbation="y")\$M0
#
ggplot(data.frame(M0y1,M0y2)) + geom_point(aes(x=M0y1, y=M0y2)) +
geom_hline(yintercept=mean(M0y2)+3.5*sd(M0y2), linetype="dashed") +
geom_vline(xintercept=mean(M0y1)+3.5*sd(M0y1), linetype="dashed") +
theme_bw()


[Package ARCensReg version 3.0.1 Index]