# mmlasso-package: Robust and Sparse Estimators for Linear Regression Models In esmucler/mmlasso: Robust and Sparse Estimators for Linear Regression Models

## Description

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.

## Details

 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 ```
 `1` ```sridge Function to calculate the S-Ridge ```

## Author(s)

Ezequiel Smucler <[email protected]>

Maintainer: Ezequiel Smucler <[email protected]>

## References

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.

## Examples

 ``` 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) ```

esmucler/mmlasso documentation built on Oct. 31, 2017, 10:55 a.m.