# SimRegDat: Simulate Incomplete Data for High-Dimensional Linear... In IROmiss: Imputation Regularized Optimization Algorithm

## Description

Simulate incomplete data for high-dimensional linear regression with dependent or independent covariates`RegICRO(x,y...)`.

## Usage

 ```1 2``` ```SimRegDat(n = 100, p = 200, coef, data.type = "indep", miss.type="MCAR", rate = 0.1) ```

## Arguments

 ` n ` Number of observations, default of 100. ` p ` Number of covariates, default of 200. ` coef ` A px1 vector of coefficients for the linear regression model. The intercept coefficient is default to 1. ` data.type ` When `data.type=="indep"`, it simulates the data with independent covariates, each covariate independently follow the normal distribution with mean 0 and variance 4. When `data.type=="dep"`, it simulates the data with dependent covariates with "band" dependent structure, see `SimGraDat` for detail. The default data type is "indep". ` miss.type ` `miss.type=="MCAR"` refer to the case of missing completely at random. when `miss.type=="MAR"`, the missing probability for each data point is proportional to the mean of its conditional normal distribution, the default missing type is "MCAR". ` rate ` Missing rate, the default value is 0.1.

## Value

 ` x ` nxp covariates matrix. ` y ` nx1 responses. ` coef ` px1 vector of coefficients for the linear regression model.

## Author(s)

Bochao Jiajbc409@ufl.edu 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 Regularized Optimization Algorithm for High-Dimensional Missing Data Problems and Beyond. Submitted to Journal of the Royal Statistical Society Series B.

## Examples

 ```1 2 3 4 5 6 7``` ```library(IROmiss) p <- 200 beta <- rep(0,p) beta[1:5] <- c(1, 2, -1.5, -2.5, 5) SimRegDat(n = 100, p = 200, coef = beta, data.type = "dep", miss.type="MAR", rate = 0.1) ```

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