to_xlist_index

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

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_xlist_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</dt>
<dd>vector of indices between 1 and nvars = sum of
<code>ncol(x)</code> for x in x_list</dd></dl>

Arguments

Translate indices in 1:nvars to column indices in list of x
matrices. No sanity checks — to_xlist_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</dt>
<dd>vector of indices between 1 and nvars = sum of
<code>ncol(x)</code> for x in x_list</dd>

</dl>

to_xlist_index: Translate indices in `1:nvars` to column indices in list of x matrices. No sanity checks

Description

Usage

Value

Arguments