fitted_values_cv

<code>fitted_values_cv</code> extracts the active set from \(X^TX\) and
\(X^Ty\) to get out-of-sample predictions
for a matrix already containing only the active variables.
The function is only relevant for cases where at least one variable is selected.

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

fitted_values_cv function

<dl><dt>XtX_all</dt>
<dd>Cross product of all covariates</dd>
<dt>Xty_all</dt>
<dd>Cross product of covariates and outcome</dd>
<dt>x_pred</dt>
<dd>Covariates matrix of the prediction sample</dd>
<dt>nm_act</dt>
<dd>Names of active variables</dd></dl>

Arguments

Fitted values for a subset of active variables — fitted_values_cv

<dl>

<dt>XtX_all</dt>
<dd>Cross product of all covariates</dd>


<dt>Xty_all</dt>
<dd>Cross product of covariates and outcome</dd>


<dt>x_pred</dt>
<dd>Covariates matrix of the prediction sample</dd>


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

</dl>

fitted_values_cv: Fitted values for a subset of active variables

Description

Usage

Value

Arguments