mus_glm

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

mus_glm function

<dl><dt>W</dt>
<dd>Design matrix, measured with error.</dd>
<dt>y</dt>
<dd>Vector of the binomial response value.</dd>
<dt>lambda</dt>
<dd>Regularization parameter due to model error.</dd>
<dt>delta</dt>
<dd>Regularization parameter due to measurement error.</dd>
<dt>family</dt>
<dd>"binomial" or "poisson"</dd>
<dt>weights</dt>
<dd>Case weights.</dd></dl>

Arguments

Generalized Matrix Uncertainty Selector for logistic regression — mus_glm

<dl>

<dt>W</dt>
<dd>Design matrix, measured with error.</dd>


<dt>y</dt>
<dd>Vector of the binomial response value.</dd>


<dt>lambda</dt>
<dd>Regularization parameter due to model error.</dd>


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


<dt>family</dt>
<dd>"binomial" or "poisson"</dd>


<dt>weights</dt>
<dd>Case weights.</dd>

</dl>

mus_glm: Generalized Matrix Uncertainty Selector for logistic regression

Description

Usage

Value

Arguments