eye_norm

Normalized Gene expression data from the microarray experiments of mammalian-eye tissue samples of Scheetz et al. (2006). It should be used in <code>EyeICRO(x,y...)</code>.<dl class="dl-horizontal">
 <dt> x </dt><dd>a nxp gene expression data.</dd>
 <dt> y </dt><dd>The expression level of gene TRIM32.</dd>
</dl>

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

eye_norm function

A list containing the matrix <code>x</code> and response matrix
 <code>y</code>

Format

Normalized Gene expression data from the microarray experiments of mammalian-eye tissue samples of Scheetz et al. (2006). It should be used in <code>EyeICRO(x,y...)</code>.<dl class='dl-horizontal'>
 <dt> x </dt><dd>a nxp gene expression data.</dd>
 <dt> y </dt><dd>The expression level of gene TRIM32.</dd>
</dl>

eye_norm: Example dataset for high-dimensional variable selection by the ICRO algorithm.

Description

Usage

Arguments

Format

References