SimRCLM: Simulate Dataset for Random Coefficient Linear Models

In IROmiss: Imputation Regularized Optimization Algorithm

Description

Simulate a dataset for random coefficient linear model, which can be used in RCLM(I,J,RCDat,...).

Usage

SimRCLM(I=100, J=10, beta, sigma)

Arguments

I	Number of first subjects in the random coefficient linear model (RCLM).
J	Number of second subjects in the random coefficient linear model (RCLM).
beta	A 4x1 vector of random coefficients of the model, now only allows length 4.
sigma	The standard diviation for the noise term.

Value

D	A simulated data matrix for random coefficient models. The first column of the dataset denote the response \mathbf{y}. The dataset should be used in RCLM(I,J,RCDat...).
coef	The mean of estimated coefficients \mathbf{β} and σ^2.

Author(s)

Bochao [e-mail] and Faming Liang

References

Liang, F., Song, Q. and Qiu, P. (2015). An Equivalent Measure of Partial Correlation Coefficients for High Dimensional Gaussian Graphical Models. J. Amer. Statist. Assoc., 110, 1248-1265.

Liang, F. and Zhang, J. (2008) Estimating FDR under general dependence using stochastic approximation. Biometrika, 95(4), 961-977.

Liang, F., Jia, B., Xue, J., Li, Q., and Luo, Y. (2018). An Imputation Penalized Optimization Algorithm for High-Dimensional Missing Data Problems and Beyond. Submitted to Journal of the Royal Statistical Society Series B.

Examples

library(IROmiss)
beta<-c(1,2,1.5,1)
sigma <- 0.5
D <- SimRCLM(I=100, J=10, beta, sigma)
RCLM(I=100, J=10, D, iteration = 10000, warm = 1000)

IROmiss documentation built on March 26, 2020, 5:56 p.m.