enetLTS (version 0.1.0)

weights.enetLTS: binary weights from the "enetLTS" object

Description

Extract binary weights that indicate outliers from the current model.

Usage

# S3 method for enetLTS
weights(object,vers=c("reweighted","raw","both"),index=FALSE,...)

Arguments

object

the model fit from which to extract outlier weights.

vers

a character string specifying for which estimator to extract outlier weights. Possible values are "reweighted" (the default) for weights indicating outliers from the reweighted fit, "raw" for weights indicating outliers from the raw fit, or "both" for the outlier weights from both estimators.

index

a logical indicating whether the indices of the weight vector should be included or not (the default is FALSE).

…

additional arguments from the enetLTS object if needed.

Value

A numeric vector containing the requested outlier weights.

See Also

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)
weights(fit1)
weights(fit1,vers="raw",index=TRUE)
weights(fit1,vers="both",index=TRUE)
# }
# 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)
weights(fit2)
weights(fit2,vers="raw",index=TRUE)
weights(fit2,vers="both",index=TRUE)
# }