wle (version 0.5)

plot.wle.cp: Plot the Weighted Mallows Cp

Description

Plot the weighted Mallows Cp based on weighted likelihood.

Usage

plot.wle.cp(object, base.line=0, num.max=20, 
           plot.it=TRUE, log.scale=FALSE, 
           xlab="Number of Predictors", ylab=NULL)

Arguments

object

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.

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.

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.

See Also

wle.cp a function to calculate the Weighted Mallows Cp, wle.lm a function for estimating linear models with normal distribution error and normal kernel.

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)