compute_pliable

Compute the interaction part of the model.

This system allows one to model a multi-variate, multi-response
problem with interaction effects. It combines the usual squared error loss
for the multi-response problem with some penalty terms to encourage
responses that correlate to form groups and also allow for modeling main and
interaction effects that exit within the covariates.
The optimization method employed is the Alternating Direction Method of
Multipliers (ADMM). The implementation is based on the methodology
presented on Quachie Asenso, T., & Zucknick, M. (2023)
<doi:10.48550/arXiv.2303.11155>.

Waldir Leoncio

MADMMplasso

Multi Variate Multi Response ADMM with Interaction Effects

Theophilus Quachie Asenso

Manuela Zucknick

Chi Zhang

compute_pliable function

<dl><dt>X</dt>
<dd>N by p matrix of predictors</dd>
<dt>Z</dt>
<dd>N by K matrix of modifying variables. The elements of Z may represent quantitative or categorical variables, or a mixture of the two.
Categorical variables should be coded by 0-1 dummy variables: for a k-level variable, one can use either k or k-1 dummy variables.</dd>
<dt>theta</dt>
<dd>theta coefficients for a single response ncol(X) by ncol(Z)</dd></dl>

Arguments

Compute the interaction part of the model. — compute_pliable

<dl>

<dt>X</dt>
<dd>N by p matrix of predictors</dd>


<dt>Z</dt>
<dd>N by K matrix of modifying variables. The elements of Z may represent quantitative or categorical variables, or a mixture of the two.
Categorical variables should be coded by 0-1 dummy variables: for a k-level variable, one can use either k or k-1 dummy variables.</dd>


<dt>theta</dt>
<dd>theta coefficients for a single response ncol(X) by ncol(Z)</dd>

</dl>

compute_pliable: Compute the interaction part of the model.

Description

Usage

Value

Arguments