IROmiss (version 1.0.2)

SimRCLM: Simulate Dataset for Random Coefficient Linear Models

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 \(\mathbf{\beta}\) and \(\sigma^2\).

%% ...

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

# NOT RUN {
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)
# }
# NOT RUN {
# }