magging: Maximin Aggregation

In FRESHD: Fast Robust Estimation of Signals in Heterogeneous Data

Maximin Aggregation

Description

R wrapper for a C++ implementation of the generic maximin aggregation procedure.

Usage

magging(B)

Arguments

B	array of size p \times G containing the group parameter estimates where p is the number of model parameters and G is the number of groups.

Details

Following Buhlmann 2016 this function computes the maximin aggregation estimator for given group estimates. This entails solving a convex quadratic optimization problem. The function wraps a C++ implementation of an algorithm by Goldfarb and Idnani for solving a (convex) quadratic programming problem by means of a dual method.

The underlying C++ program solving the convex quadratic optimization problem, eiquadprog.hpp, copyright (2011) Benjamin Stephens, GPL v2 see https://www.cs.cmu.edu/~bstephe1/eiquadprog.hpp, is based on previous libraries:

QuadProg++, Copyright (C) 2007-2016 Luca Di Gaspero, MIT License. See https://github.com/liuq/QuadProgpp

uQuadProg, Copyright (C) 2006 - 2017 Angelo Furfaro, LGPL v3, a port of QuadProg++ working with ublas data structures. See https://github.com/fx74/uQuadProg/blob/master/README.md

QuadProg Copyright (C) 2014-2015 Gael Guennebaud, LGPL v3, a modification of uQuadProg, working with Eigen data structures. See http://www.labri.fr/perso/guenneba/code/QuadProg/.

Value

An object with S3 Class "FRESHD".

...	A p vector containing the maximin aggregated parameter estimates.

Author(s)

Adam Lund, Benjamin Stephens, Gael Guennebaud, Angelo Furfaro, Luca Di Gaspero

Maintainer: Adam Lund, [e-mail]

References

Buhlmann, Peter and Meinshausen, Nicolai (2016). Magging: maximin aggregation for inhomogeneous large-scale data. Proceedings of the IEEE, 1, 104, 126-135

D. Goldfarb, A. Idnani. A numerically stable dual method for solving strictly convex quadratic programs (1983). Mathematical Programming, 27, 1-33.

Examples

##size of example
set.seed(42)
G <- 15; n <- c(50, 20, 13); p <- c(7, 5, 4)
nlambda <- 10

##marginal design matrices (Kronecker components)
x <- list()
for(i in 1:length(n)){
x[[i]] <- matrix(rnorm(n[i] * p[i], 0, 1), n[i], p[i])
}

##common features and effects
common_features <- rbinom(prod(p), 1, 0.1)
common_effects <- rnorm(prod(p), 0, 1) * common_features
system.time({
##group response and fit
lambda <- exp(seq(-1, -4, length.out = nlambda))
magbeta <- matrix(0, prod(p), nlambda)
B <- array(NA, c(prod(p), G, nlambda))
y <- array(NA, c(n, G))
for(g in 1:G){
bg <- rnorm(prod(p), 0, 0.1) * (1 - common_features) + common_effects
Bg <- array(bg, p)
mu <- RH(x[[3]], RH(x[[2]], RH(x[[1]], Bg)))
y[,,, g] <- array(rnorm(prod(n)), dim = n) + mu
B[, g, ] <- glamlasso::glamlasso(x, y[,,, g], lambda = lambda)$coef
}
})

##maximin aggregation for all lambdas (models)
for(l in 1:dim(B)[3]){
magbeta[, l] <- magging(B[, , l])
}

##estimated common effects for specific lambda
modelno <- 10
betafit <- magbeta[, modelno]
plot(common_effects, type = "h", ylim = range(betafit, common_effects), col = "red")
lines(betafit, type = "h")

FRESHD documentation built on May 12, 2022, 9:06 a.m.