R Graphical Manual

Browse All

Last data update: 2014.03.03

R: Plot the Weighted Mallows Cp

plot.wle.cp

R Documentation

Plot the Weighted Mallows Cp

Description

Plot the weighted Mallows Cp based on weighted likelihood.

Usage

## S3 method for class 'wle.cp'
plot(x, base.line=0, num.max=20, 
           plot.it=TRUE, log.scale=FALSE, 
           xlab="Number of Predictors", ylab=NULL,
           verbose=FALSE, ...)

Arguments

`x`	an object of class `wle.cp`.
`base.line`	the intercept of the line to split the submodels in acceptable (good) and not-acceptable (bad), (the slope is always one).
`num.max`	maximum number of submodels plotted.
`plot.it`	if TRUE the graph is plotted.
`log.scale`	if TRUE the y-axis as log10 scale.
`xlab`	a title for the x axis.
`ylab`	a title for the y axis.
`verbose`	if `TRUE` warnings are printed.
`...`	graphical parameters can be given as arguments.

Value

`num.good`	number of submodels below the `base.line`
`num.bad`	number of submodels above the `base.line`
`wcp.good`	list of the submodels below the `base.line` with their WCp.
`wcp.bad`	list of the submodels above the `base.line` with their WCp.

Author(s)

Claudio Agostinelli

References

Agostinelli, C., (1999) Robust model selection in regression via weighted likelihood methodology, Working Paper n. 1999.4, Department of Statistics, Universiy of Padova.

Agostinelli, C., (1999) Robust model selection in regression via weighted likelihood methodology, submitted to Statistics & Probability Letters, revised december 1999.

Agostinelli, C., (1998) Inferenza statistica robusta basata sulla funzione di verosimiglianza pesata: alcuni sviluppi, Ph.D Thesis, Department of Statistics, University of Padova.

Agostinelli, C., (1998) Verosimiglianza pesata nel modello di regressione lineare, XXXIX Riunione scientifica della Societ'a Italiana di Statistica, Sorrento 1998.

Examples

library(wle)
x.data <- c(runif(60,20,80),runif(5,73,78))
e.data <- rnorm(65,0,0.6)
y.data <- 8*log(x.data+1)+e.data
y.data[61:65] <- y.data[61:65]-4
z.data <- c(rep(0,60),rep(1,5))
plot(x.data, y.data, xlab="X", ylab="Y")
xx.data <- cbind(x.data, x.data^2, x.data^3, log(x.data+1))
result <- wle.cp(y.data~xx.data)
plot(result,num.max=15)