plotCoef.enetLTS

0th

Percentile

coefficients plots from the "enetLTS" object

Produce plots for the coefficients of the current model.

Keywords
regression, classification
Usage
plotCoef.enetLTS(object,vers=c("reweighted","raw"),colors=NULL,...)
Arguments
object

the model fit to be plotted.

vers

a character string denoting which model to use for the plots. Possible values are "reweighted" (the default) for plots from the reweighted fit, and "raw" for plots from the raw fit.

colors

optional parameter, list object with list names bars, errorbars, background, abline, scores, cutoffs, badouts, modouts, each containing a string referring to a color.

additional arguments from the enetLTS object if needed.

Value

An object of class "ggplot" (see ggplot).

References

Kurnaz, F.S., Hoffmann, I. and Filzmoser, P. (2017) Robust and sparse estimation methods for high dimensional linear and logistic regression. Chemometrics and Intelligent Laboratory Systems.

See Also

ggplot, enetLTS, coef.enetLTS, predict.enetLTS

Aliases
  • plotCoef.enetLTS
Examples
# NOT RUN {
## for gaussian

set.seed(86)
n <- 100; p <- 25                             # number of observations and variables
beta <- rep(0,p); beta[1:6] <- 1              # 10% nonzero coefficients
sigma <- 0.5                                  # controls signal-to-noise ratio
x <- matrix(rnorm(n*p, sigma),nrow=n)
e <- rnorm(n,0,1)                             # error terms
eps <- 0.1                                    # contamination level
m <- ceiling(eps*n)                           # observations to be contaminated
eout <- e; eout[1:m] <- eout[1:m] + 10        # vertical outliers
yout <- c(x %*% beta + sigma * eout)          # response
xout <- x; xout[1:m,] <- xout[1:m,] + 10      # bad leverage points

# }
# NOT RUN {
fit1 <- enetLTS(xout,yout,alphas=0.5,lambdas=0.05,plot=FALSE)
plotCoef.enetLTS(fit1)
plotCoef.enetLTS(fit1,vers="raw")
# }
# NOT RUN {
## for binomial
eps <-0.05                                     # %10 contamination to only class 0
m <- ceiling(eps*n)
y <- sample(0:1,n,replace=TRUE)
xout <- x
xout[y==0,][1:m,] <- xout[1:m,] + 10;          # class 0
yout <- y                                      # wrong classification for vertical outliers

# }
# NOT RUN {
fit2 <- enetLTS(xout,yout,family="binomial",alphas=0.5,lambdas=0.05,plot=FALSE)
plotCoef.enetLTS(fit2)
plotCoef.enetLTS(fit2,vers="raw")
# }
# NOT RUN {
# }
Documentation reproduced from package enetLTS, version 0.1.0, License: GPL (>= 3)

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