weights.sparseLTS {robustHD}

Extract outlier weights from sparse LTS regression models

Description

Extract binary weights that indicate outliers from sparse least trimmed squares regression models.

Usage

## S3 method for class 'sparseLTS'
weights(
  object,
  type = "robustness",
  s = NA,
  fit = c("reweighted", "raw", "both"),
  drop = !is.null(s),
  ...
)

Arguments

object

the model fit from which to extract outlier weights.

type

the type of weights to be returned. Currently only robustness weights are implemented ("robustness").

s

an integer vector giving the indices of the models for which to extract outlier weights. If fit is "both", this can be a list with two components, with the first component giving the indices of the reweighted fits and the second the indices of the raw fits. The default is to use the optimal model for each of the requested estimators. Note that the optimal models may not correspond to the same value of the penalty parameter for the reweighted and the raw estimator.

fit

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.

drop

a logical indicating whether to reduce the dimension to a vector in case of only one model.

...

currently ignored.

Value

A numeric vector or matrix containing the requested outlier weights.

Note

The weights are 1 for observations with reasonably small residuals and 0 for observations with large residuals.

Author(s)

Andreas Alfons

See Also

sparseLTS

Examples

## generate data
# example is not high-dimensional to keep computation time low
library("mvtnorm")
set.seed(1234)  # for reproducibility
n <- 100  # number of observations
p <- 25   # number of variables
beta <- rep.int(c(1, 0), c(5, p-5))  # coefficients
sigma <- 0.5      # controls signal-to-noise ratio
epsilon <- 0.1    # contamination level
Sigma <- 0.5^t(sapply(1:p, function(i, j) abs(i-j), 1:p))
x <- rmvnorm(n, sigma=Sigma)    # predictor matrix
e <- rnorm(n)                   # error terms
i <- 1:ceiling(epsilon*n)       # observations to be contaminated
e[i] <- e[i] + 5                # vertical outliers
y <- c(x %*% beta + sigma * e)  # response
x[i,] <- x[i,] + 5              # bad leverage points

## sparse LTS over a grid of values for lambda
# fit model
frac <- seq(0.2, 0.05, by = -0.05)
fitGrid <- sparseLTS(x, y, lambda = frac, mode = "fraction")
# extract outlier weights
weights(fitGrid)
head(weights(fitGrid, fit = "both"))
head(weights(fitGrid, s = NULL))
head(weights(fitGrid, fit = "both", s = NULL))