# weights.enetLTS

##### binary weights from the `"enetLTS"`

object

Extract binary weights that indicate outliers from the current model.

- Keywords
- regression, classification

##### 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.

##### Note

The weights are \(1\) for observations with reasonably small residuals and \(0\) for observations with large residuals. Here, residuals represent standardized residuals for linear regression and Pearson residuals for logistic residuals.

Use weights with or without index is available.

##### See Also

##### 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)
# }
```

*Documentation reproduced from package enetLTS, version 0.1.0, License: GPL (>= 3)*