relative_error

Calculate relative error between estimated and true matrices

Transfer learning for generalized factor models with support for continuous, count (Poisson), and binary data types. The package provides functions for single and multiple source transfer learning, source detection to identify positive and negative transfer sources, factor decomposition using Maximum Likelihood Estimation (MLE), and information criteria ('IC1' and 'IC2') for rank selection. The methods are particularly useful for high-dimensional data analysis where auxiliary information from related source datasets can improve estimation efficiency in the target domain.

Zhijing Wang

transGFM

Transfer Learning for Generalized Factor Models

Peirong Xu

Hongyu Zhao

Tao Wang

relative_error function

<dl><dt>M_hat</dt>
<dd>Estimated matrix</dd>
<dt>M_true</dt>
<dd>True matrix</dd></dl>

Arguments

Calculate relative error between estimated and true matrices — relative_error

<dl>

<dt>M_hat</dt>
<dd>Estimated matrix</dd>


<dt>M_true</dt>
<dd>True matrix</dd>

</dl>

relative_error: Calculate relative error between estimated and true matrices

Description

Usage

Value

Arguments

Examples