PrimaryCancers

Data set consisting of microRNA normalized expression measurements of primary cancer samples.

data

Multinomial logistic regression with sparse group lasso
penalty. Simultaneous feature selection and parameter
estimation for classification. Suitable for high dimensional
multiclass classification with many classes. The algorithm
computes the sparse group lasso penalized maximum likelihood
estimate. Use of parallel computing for cross validation and
subsampling is supported through the 'foreach' and 'doParallel'
packages. Development version is on GitHub, please report
package issues on GitHub.

Niels Richard Hansen

msgl

Multinomial Sparse Group Lasso

Martin Vincent

PrimaryCancers function

A design matrix and a class vector<dl class="dl-horizontal">
 <dt>x</dt><dd>design matrix</dd>
 <dt>classes</dt><dd>class vector</dd>
</dl>

Format

Primary cancer samples. — PrimaryCancers

A design matrix and a class vector<dl class='dl-horizontal'>
 <dt>x</dt><dd>design matrix</dd>
 <dt>classes</dt><dd>class vector</dd>
</dl>

PrimaryCancers: Primary cancer samples.

Description

Arguments

Format

References