README.md

In dkucharc/glmSLOPE: Sorted L1 Penalized Estimation (SLOPE) for generalized linear models (GLM)

glmSLOPE - SLOPE for generalized linear models

Instalation guide

Currently the package is under heavy development. Therefore, it is recommended to install the package directly from the Github repository. This can achieved by using an install_github function provided by the devtools package. Namely,

library(devtools)
install_github("dkucharc/glmSLOPE")

Examples

• Linear model

library(glmSLOPE)
# Test data
X <- c(0.53766714, 1.833885, -2.2588469, 0.86217332, 0.31876524, -1.3076883, -0.43359202, 0.34262447, 3.5783969, 2.769437, -1.3498869, 3.0349235, 0.72540422, -0.063054873, 0.7147429, -0.20496606, -0.12414435, 1.4896976, 1.4090345, 1.4171924, 0.67149713, -1.2074869, 0.71723865, 1.6302353, 0.48889377, 1.034693, 0.72688513, -0.30344092, 0.29387147, -0.7872828, 0.88839563, -1.1470701, -1.0688705, -0.80949869, -2.9442842, 1.4383803, 0.32519054, -0.75492832, 1.3702985, -1.7115164, -0.10224245, -0.24144704, 0.31920674, 0.3128586, -0.86487992, -0.030051296, -0.16487902, 0.62770729, 1.0932657, 1.109273)
dim(X) <- c(5, 10)
y <- c(1.0734014, -5.3021346, 1.096639, -0.39124089, -0.92884291)
dim(y) <- c(5,1)
lambda <- c(1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1)
# Estimate parameters
solve_slope(X, y, lambda, model = 'linear')

• Logistic model

library(glmSLOPE)
# Test data
X <- c(0.53766714, 1.833885, -2.2588469, 0.86217332, 0.31876524, -1.3076883, -0.43359202, 0.34262447, 3.5783969, 2.769437, -1.3498869, 3.0349235, 0.72540422, -0.063054873, 0.7147429, -0.20496606, -0.12414435, 1.4896976, 1.4090345, 1.4171924, 0.67149713, -1.2074869, 0.71723865, 1.6302353, 0.48889377, 1.034693, 0.72688513, -0.30344092, 0.29387147, -0.7872828, 0.88839563, -1.1470701, -1.0688705, -0.80949869, -2.9442842, 1.4383803, 0.32519054, -0.75492832, 1.3702985, -1.7115164, -0.10224245, -0.24144704, 0.31920674, 0.3128586, -0.86487992, -0.030051296, -0.16487902, 0.62770729, 1.0932657, 1.109273)
dim(X) <- c(5, 10)
y <- c(1, -1, 1, -1, -1)
dim(y) <- c(5,1)
lambda <- c(1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1)
# Estimate parameters
solve_slope(X, y, lambda, model = 'logistic')

References:

1. SLOPE—Adaptive variable selection via convex optimization

2. Sparse Portfolio Selection via the sorted L1 - Norm

3. Proximal Algorithms

dkucharc/glmSLOPE documentation built on May 21, 2019, 1:43 p.m.