WorkingResp

Calculate the working response in the iterative least square regression

It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion.

Wencan Zhu

WLogit

Variable Selection in High-Dimensional Logistic Regression
Models using a Whitening Approach

WorkingResp function

<dl> <dt>X</dt>
<dd>Design matrix of the logistic model considered.</dd> <dt>y</dt>
<dd>Binary response of the logistic model considered.</dd> <dt>Px</dt>
<dd>The probability of the reponse to be 1</dd>
 <dt>beta</dt>
<dd>Vector of coefficients</dd> <dt>intercept</dt>
<dd>If there is an intercept</dd></dl>

Arguments

Wencan Zhu, Celine Levy-Leduc, Nils Ternes

Author

Calculate the working response — WorkingResp

<dl>

 <dt>X</dt>
<dd>Design matrix of the logistic model considered.</dd>

 <dt>y</dt>
<dd>Binary response of the logistic model considered.</dd>

 <dt>Px</dt>
<dd>The probability of the reponse to be 1</dd>


 <dt>beta</dt>
<dd>Vector of coefficients</dd>

 <dt>intercept</dt>
<dd>If there is an intercept</dd>

</dl>

WorkingResp: Calculate the working response

Description

Usage

Value

Arguments

Author

See Also