standardize_wt

Performs weighted standardization of x variables. Used in <code>fastridge</code>.

A set of tools that enables efficient estimation of penalized
Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models
that feature one or more sets of high-dimensional fixed effects. The methodology is based on
Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) <http://hdl.handle.net/10986/35451>
and takes advantage of the method of alternating projections of Gaure (2013)
<doi:10.1016/j.csda.2013.03.024> for dealing with HDFE, as well as
the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010)
<doi:10.18637/jss.v033.i01> for fitting lasso regressions. The package is also able to carry out
cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016)
<doi:10.1080/07350015.2015.1102733>.

Joao Cruz

penppml

Penalized Poisson Pseudo Maximum Likelihood Regression

Diego Ferreras Garrucho

Tom Zylkin

Nicolas Apfel

standardize_wt function

<dl><dt>x</dt>
<dd>Regressor matrix.</dd>
<dt>weights</dt>
<dd>Weights.</dd>
<dt>intercept</dt>
<dd>Logical. If <code>TRUE</code>, adds an intercept.</dd>
<dt>return.sd</dt>
<dd>Logical. If <code>TRUE</code>, it returns standard errors for the means.</dd></dl>

Arguments

Weighted Standardization — standardize_wt

<dl>

<dt>x</dt>
<dd>Regressor matrix.</dd>


<dt>weights</dt>
<dd>Weights.</dd>


<dt>intercept</dt>
<dd>Logical. If <code>TRUE</code>, adds an intercept.</dd>


<dt>return.sd</dt>
<dd>Logical. If <code>TRUE</code>, it returns standard errors for the means.</dd>

</dl>

standardize_wt: Weighted Standardization

Description

Usage

Value

Arguments