sparseLTSEigen (version 0.2.0.1)

sparseLTSEigen-package: RcppEigen back end for sparse least trimmed squares regression

Description

Use RcppEigen to fit least trimmed squares regression models with an L1 penalty in order to obtain sparse models.

Details

Package:	sparseLTSEigen
Type:	Package
Version:	0.2.0
Date:	2013-11-13
Depends:	robustHD (>= 0.4.0)
Imports:	Rcpp (>= 0.9.10), RcppEigen (>= 0.2.0)
Suggests:	mvtnorm
LinkingTo:	Rcpp, RcppEigen
License:	GPL (>= 2)
LazyLoad:	yes

Index:

sparseLTSEigen-package
                        RcppEigen back end for sparse least trimmed
                        squares regression

Examples

# NOT RUN {
# 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

## fit sparse LTS model
# since package sparseLTSEigen is loaded, its back end based on 
# the C++ library Eigen is used rather than the back end built 
# into package robustHD, except on 32-bit R for Windows
fit <- sparseLTS(x, y, lambda = 0.05, mode = "fraction")
coef(fit, zeros = FALSE)
# }