RCLM: Fit Random Coefficient Linear Models

In IROmiss: Imputation Regularized Optimization Algorithm

Description

An extension of the ICRO algorithm for Bayesian Computation. It can be used to fit a Random Coefficient Linear Models and estimate the coefficients β and σ^2.

Usage

RCLM(I=100, J=10, Data, iteration = 10000, warm = 100)

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).
Data	A simulated dataset. The first column is the response and the rest is for explanatory variables, see RCDat for detail.
iteration	The number of total iterations, the default value is 10000.
warm	The number of burn-in iterations, the default value is 100.

Value

path	The traces of estimated coefficients vs. iterations.
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)
data(RCDat)
RCLM(I=100, J=10, RCDat, iteration = 10000, warm = 1000)


         

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