Description Details Author(s) References Examples
Functions to calculate the MM-Lasso and adaptive MM-Lasso estimators proposed in Smucler and Yohai (2015). The S-Ridge estimator of Maronna (2011) is used as the initial estimator.
Package: | mmlasso |
Type: | Package |
Version: | 1.3.4 |
Date: | 2016-2-26 |
License: | GPL (>= 2) |
Imports: | Rcpp, robustHD, robustbase, parallel, doParallel, foreach, MASS |
LinkingTo: | Rcpp, RcppArmadillo |
NeedsCompilation: | yes |
Index:
1 2 | mmlasso Function to calculate the adaptive MM-Lasso
and the MM-Lasso
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1 | sridge Function to calculate the S-Ridge
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Ezequiel Smucler <ezequiels.90@gmail.com>
Maintainer: Ezequiel Smucler <ezequiels.90@gmail.com>
Ezequiel Smucler and Victor J. Yohai. Robust and sparse estimators for linear regression models (2015). Available at http://arxiv.org/abs/1508.01967.
Maronna, R.A. (2011). Robust Ridge Regression for High-Dimensional Data. Technometrics 53 44-53.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | require(MASS)
p <- 8
n <- 60
rho <- 0.5
desv <- 1
beta.true <- c(rep(0,p+1))
beta.true[2] <- 3
beta.true[3] <- 1.5
beta.true[7] <- 2
mu <- rep(0,p)
sigma <- rho^t(sapply(1:p, function(i, j) abs(i-j), 1:p))
set.seed(1234)
x <- mvrnorm(n,mu,sigma)
u <- rnorm(n)*desv
y <- x%*%beta.true[2:(p+1)]+beta.true[1]+u
###Calculate estimators
set.seed(1234)
RobSparse <- mmlasso(x,y)
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