evaluation.sum

The main function for Transfer learning for tensor graphical models.

In medical research, supervised heterogeneity analysis has important implications. Assume that there are two types of features. Using both types of features, our goal is to conduct the first supervised heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization.
Reference:
Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S. (2022). "Hierarchical cancer heterogeneity analysis based on histopathological imaging features". Biometrics, <doi:10.1111/biom.13426>.

Mingyang Ren

Hierarchical Heterogeneity Analysis via Penalization

Qingzhao Zhang

Sanguo Zhang

Tingyan Zhong

Jian Huang

Shuangge Ma

evaluation.sum function

<dl><dt>n</dt>
<dd>The sample size.</dd>
<dt>q</dt>
<dd>The dimension of type 1 features.</dd>
<dt>p</dt>
<dd>The dimension of type 2 features.</dd>
<dt>admmres</dt>
<dd>The results corresponding to lambda1.</dd>
<dt>abic.n</dt>
<dd>The BIC values.</dd>
<dt>admmres2</dt>
<dd>The results corresponding to lambda1.</dd>
<dt>Beta0</dt>
<dd>The true values of beta.</dd>
<dt>bic.var</dt>
<dd>The BIC values.</dd></dl>

Arguments

Hierarchical Heterogeneity Regression Analysis. — evaluation.sum

<dl>

<dt>n</dt>
<dd>The sample size.</dd>


<dt>q</dt>
<dd>The dimension of type 1 features.</dd>


<dt>p</dt>
<dd>The dimension of type 2 features.</dd>


<dt>admmres</dt>
<dd>The results corresponding to lambda1.</dd>


<dt>abic.n</dt>
<dd>The BIC values.</dd>


<dt>admmres2</dt>
<dd>The results corresponding to lambda1.</dd>


<dt>Beta0</dt>
<dd>The true values of beta.</dd>


<dt>bic.var</dt>
<dd>The BIC values.</dd>

</dl>

evaluation.sum: Hierarchical Heterogeneity Regression Analysis.

Description

Usage

Value

Arguments