print.gmu_lasso

Default print method for a <code>gmu_lasso</code> object.

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

print.gmu_lasso function

<dl><dt>x</dt>
<dd>Fitted model object returned by <code>gmu_lasso</code>.</dd>
<dt>...</dt>
<dd>Other arguments (not used).</dd></dl>

Arguments

Print a GMU Lasso object — print.gmu_lasso

<dl>

<dt>x</dt>
<dd>Fitted model object returned by <code>gmu_lasso</code>.</dd>


<dt>...</dt>
<dd>Other arguments (not used).</dd>

</dl>

print.gmu_lasso: Print a GMU Lasso object

Description

Usage

Arguments