cox_obj_function

Returns the elastic net objective function value for Cox regression model.

Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).

Balasubramanian Narasimhan

multiview

Cooperative Learning for Multi-View Analysis

Daisy Yi Ding 

Robert J. Tibshirani

Trevor Hastie

Kenneth Tay

James Yang

cox_obj_function function

<dl><dt>y</dt>
<dd>Survival response variable, must be a <code>Surv</code> or
<code>stratifySurv</code> object.</dd>
<dt>pred</dt>
<dd>Model's predictions for <code>y</code>.</dd>
<dt>weights</dt>
<dd>Observation weights.</dd>
<dt>lambda</dt>
<dd>A single value for the <code>lambda</code> hyperparameter.</dd>
<dt>alpha</dt>
<dd>The elasticnet mixing parameter, with \(0 \le \alpha \le 1\).</dd>
<dt>coefficients</dt>
<dd>The model's coefficients.</dd>
<dt>vp</dt>
<dd>Penalty factors for each of the coefficients.</dd>
<dt>view_components</dt>
<dd>a list of lists containing indices of coefficients and associated covariate (view) pairs</dd>
<dt>rho</dt>
<dd>the fusion parameter</dd></dl>

Arguments

Elastic net objective function value for Cox regression model — cox_obj_function

<dl>

<dt>y</dt>
<dd>Survival response variable, must be a <code>Surv</code> or
<code>stratifySurv</code> object.</dd>


<dt>pred</dt>
<dd>Model's predictions for <code>y</code>.</dd>


<dt>weights</dt>
<dd>Observation weights.</dd>


<dt>lambda</dt>
<dd>A single value for the <code>lambda</code> hyperparameter.</dd>


<dt>alpha</dt>
<dd>The elasticnet mixing parameter, with \(0 \le \alpha \le 1\).</dd>


<dt>coefficients</dt>
<dd>The model's coefficients.</dd>


<dt>vp</dt>
<dd>Penalty factors for each of the coefficients.</dd>


<dt>view_components</dt>
<dd>a list of lists containing indices of coefficients and associated covariate (view) pairs</dd>


<dt>rho</dt>
<dd>the fusion parameter</dd>

</dl>

cox_obj_function: Elastic net objective function value for Cox regression model

Description

Usage

Arguments