to_nvar_index

Translate from column indices in list of x matrices to indices in
<code>1:nvars</code>. No sanity checks for efficiency

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

to_nvar_index function

<dl><dt>x_list</dt>
<dd>a list of <code>x</code> matrices with same number of rows
<code>nobs</code></dd>
<dt>index_list</dt>
<dd>a list of column indices for each matrix,
including possibly column indices of length 0</dd></dl>

Arguments

Translate from column indices in list of x matrices to indices in
1:nvars. No sanity checks for efficiency — to_nvar_index

<dl>

<dt>x_list</dt>
<dd>a list of <code>x</code> matrices with same number of rows
<code>nobs</code></dd>


<dt>index_list</dt>
<dd>a list of column indices for each matrix,
including possibly column indices of length 0</dd>

</dl>

to_nvar_index: Translate from column indices in list of x matrices to indices in `1:nvars`. No sanity checks for efficiency

Description

Usage

Value

Arguments