musalgorithm

internal

Penalized regression for generalized linear models for
measurement error problems (aka. errors-in-variables). The package
contains a version of the lasso (L1-penalization) which corrects
for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>).
It also contains an implementation of the Generalized Matrix Uncertainty
Selector, which is a version the (Generalized) Dantzig Selector for the
case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).

Oystein Sorensen

hdme

High-Dimensional Regression with Measurement Error

musalgorithm function

<dl><dt>W</dt>
<dd>Matrix of measurements.</dd>
<dt>y</dt>
<dd>Response vector.</dd>
<dt>lambda</dt>
<dd>Regularization parameter due to residual.</dd>
<dt>delta</dt>
<dd>Regularization parameter due to measurement error.</dd></dl>

Arguments

Algorithm for mus — musalgorithm

<dl>

<dt>W</dt>
<dd>Matrix of measurements.</dd>


<dt>y</dt>
<dd>Response vector.</dd>


<dt>lambda</dt>
<dd>Regularization parameter due to residual.</dd>


<dt>delta</dt>
<dd>Regularization parameter due to measurement error.</dd>

</dl>

musalgorithm: Algorithm for mus

Description

Usage

Arguments