yeast

Genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental changes. The whole dataset consists of 173 samples collected under different environmental settings, and is available at http://www-genome.stanford.edu/yeast_stress/. It should be used in <code>YeastIRO(data,...)</code>.

datasets

Missing data are frequently encountered in high-dimensional data analysis, but they are usually difficult to deal with using standard algorithms, such as the EM algorithm and its variants. This package provides a general algorithm, the so-called Imputation Regularized Optimization (IRO) algorithm, for high-dimensional missing data problems. You can refer to Liang, F., Jia, B., Xue, J., Li, Q. and Luo, Y. (2018) at <arXiv:1802.02251> for detail.

Bochao Jia

IROmiss

Imputation Regularized Optimization Algorithm

Faming Liang

yeast function

<dl class="dl-horizontal">
 <dt> yeast </dt><dd>a nxp Yeast Cell expression data.</dd>
</dl>

Format

Example dataset for learning Gaussian Graphical Models by the IRO Algorithm — yeast

<dl class='dl-horizontal'>
 <dt> yeast </dt><dd>a nxp Yeast Cell expression data.</dd>
</dl>

yeast: Example dataset for learning Gaussian Graphical Models by the IRO Algorithm

Description

Usage

Arguments

Format

References