RCDat: A simulated dataset for random coefficient models.
In IROmiss: Imputation Regularized Optimization Algorithm
Description
Usage
Format
References
Description
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{β} is (β_0,β_1,β_2,β_3)= (1, 2, 1.5, 1) and the true value of σ^2 is 0.25. The first column of the dataset denote the response \mathbf{y}. The dataset should be used in RCLM(I,J,RCDat,...).
- RCDat
A simulated dataset.
Usage
1
Format
matrix
References
Liang, F., Jia, B., Xue, J., Li, Q., and Luo, Y. (2018). An Imputation Regularized Optimization Algorithm for High-Dimensional Missing Data Problems and Beyond. Submitted to Journal of the Royal Statistical Society Series B.
IROmiss documentation built on March 26, 2020, 5:56 p.m.
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Description Usage Format References
Description
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{β} is (β_0,β_1,β_2,β_3)= (1, 2, 1.5, 1) and the true value of σ^2 is 0.25. The first column of the dataset denote the response \mathbf{y}. The dataset should be used in RCLM(I,J,RCDat,...).
- RCDat
A simulated dataset.
Usage
1 |
Format
matrix
References
Liang, F., Jia, B., Xue, J., Li, Q., and Luo, Y. (2018). An Imputation Regularized Optimization Algorithm for High-Dimensional Missing Data Problems and Beyond. Submitted to Journal of the Royal Statistical Society Series B.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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