ARCensReg (version 3.0.1)

plot.DiagARpCRM: Plot influence diagnostic measures

Description

Plot method for objects of class "DiagARpCRM".

Usage

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

Value

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

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.

Author

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

See Also

ggplot, InfDiag, ARCensReg

Examples

# \donttest{
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()
# }