fit_betas

<code>fit_betas</code> estimates OLS model only for active coefficients (from lasso)

internal

Provides tools for cross-validated Lasso and Post-Lasso estimation.
Built on top of the 'glmnet' package by Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01>,
the main function plasso() extends the standard 'glmnet' output with coefficient paths
for Post-Lasso models, while cv.plasso() performs cross-validation for both Lasso
and Post-Lasso models and different ways to select the penalty parameter lambda as discussed in Knaus (2021) <doi:10.1111/rssa.12623>.

Knaus Michael C.

plasso

Cross-Validated Post-Lasso

Michael C. Knaus

Stefan Glaisner

fit_betas function

<dl><dt>x</dt>
<dd>Matrix of covariates (number of observations times number of covariates matrix)</dd>
<dt>y</dt>
<dd>Vector of outcomes</dd>
<dt>w</dt>
<dd>Vector of weights</dd>
<dt>nm_act</dt>
<dd>Vector of active variables</dd>
<dt>coef_lasso</dt>
<dd>Vector of lasso coefficients</dd></dl>

Arguments

plasso fitting — fit_betas

<dl>

<dt>x</dt>
<dd>Matrix of covariates (number of observations times number of covariates matrix)</dd>


<dt>y</dt>
<dd>Vector of outcomes</dd>


<dt>w</dt>
<dd>Vector of weights</dd>


<dt>nm_act</dt>
<dd>Vector of active variables</dd>


<dt>coef_lasso</dt>
<dd>Vector of lasso coefficients</dd>

</dl>

fit_betas: plasso fitting

Description

Usage

Value

Arguments