genelambda.obo

Generating a sequence of the tuning parameters (lambda1 and lambda2).

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

genelambda.obo function

<dl><dt>nlambda1</dt>
<dd>The numbers of lambda 1.</dd>
<dt>lambda1_max</dt>
<dd>The maximum values of lambda 1.</dd>
<dt>lambda1_min</dt>
<dd>The minimum values of lambda 1.</dd>
<dt>nlambda2</dt>
<dd>The numbers of lambda 2.</dd>
<dt>lambda2_max</dt>
<dd>The maximum values of lambda 2.</dd>
<dt>lambda2_min</dt>
<dd>The minimum values of lambda 2.</dd></dl>

Arguments

Author

Generate tuning parameters — genelambda.obo

<dl>

<dt>nlambda1</dt>
<dd>The numbers of lambda 1.</dd>


<dt>lambda1_max</dt>
<dd>The maximum values of lambda 1.</dd>


<dt>lambda1_min</dt>
<dd>The minimum values of lambda 1.</dd>


<dt>nlambda2</dt>
<dd>The numbers of lambda 2.</dd>


<dt>lambda2_max</dt>
<dd>The maximum values of lambda 2.</dd>


<dt>lambda2_min</dt>
<dd>The minimum values of lambda 2.</dd>

</dl>

genelambda.obo: Generate tuning parameters

Description

Usage

Value

Arguments

Author

Examples