RCDat

The dataset is generated using the default settings. The Number of customers I=100 and each customer responds to J=10 items. For the parameters, the true coefficient \(\mathbf{\beta}\) is \((\beta_0,\beta_1,\beta_2,\beta_3)= (1, 2, 1.5, 1)\) and the true value of \(\sigma^2\) is 0.25. The first column of the dataset denote the response \(\mathbf{y}\). The dataset should be used in <code>RCLM(I,J,RCDat,...)</code>.<dl class="dl-horizontal">
 <dt> RCDat </dt><dd>A simulated dataset.</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

RCDat function

Format

The dataset is generated using the default settings. The Number of customers I=100 and each customer responds to J=10 items. For the parameters, the true coefficient \(\mathbf{\beta}\) is \((\beta_0,\beta_1,\beta_2,\beta_3)= (1, 2, 1.5, 1)\) and the true value of \(\sigma^2\) is 0.25. The first column of the dataset denote the response \(\mathbf{y}\). The dataset should be used in <code>RCLM(I,J,RCDat,...)</code>.<dl class='dl-horizontal'>
 <dt> RCDat </dt><dd>A simulated dataset.</dd>
</dl>

RCDat: A simulated dataset for random coefficient models.

Description

Usage

Arguments

Format

References