Description Details Author(s) References Examples
Missing data are frequently encountered in high-dimensional data analysis, but they are usually difficult to deal with using standard algorithms, such as the EM algorithm and its variants. This package provides a general algorithm, the so-called imputation regularized optimization (IRO) algorithm, for treating high-dimensional missing data problems. A variant of the IRO algorithm, the so-called imputation conditional regularized optimization (ICRO) algorithm, has also been provided in the package.
Package: | IROmiss |
Type: | Package |
Version: | 1.0.2 |
Date: | 2020-02-19 |
License: | GPL-2 |
This package illustrates the use of the IRO/ICRO algorithms in three modules:
The first module is to apply the IRO algorithm to learning high-dimensional Gaussian Graphical Models (GGMs) in presence
of missing data with a simulated dataset SimGraDat(n,p,...)
and
Yeast cell example YeastIRO(data,...)
.
The second module is to apply the ICRO algorithm to varisable selection for high-dimensional linear regression in presence of missing data. The simulation study covers both cases, the covariates are mutually independent and generally dependent, with the code SimRegDat(n,p,...)
. The real data example is for Bardet-Biedl syndrome (Scheetz et al., 2006) with the dataset available in the R package flare.
The third module is to apply the ICRO algorithm to random coefficient linear models, where the random coefficients are treated as missing data. We can generate a dataset for the random coefficient linear models with SimRCLM(I,J,...)
and a simulated dataset data(RCDat)
is included in the package, which can be used in RCLM(I,J,RCDat,...)
for estimate the random coefficents.
Bochao Jia, Faming Liang Maintainer: Bochao Jia<jbc409@ufl.edu>
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.<doi:10.1080/01621459.2015.1012391>
Liang, F. and Zhang, J. (2008) Estimating FDR under general dependence using stochastic approximation. Biometrika, 95(4), 961-977.<doi:10.1093/biomet/asn036>
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. <arXiv:1802.02251>
Jia, B., Xu, S., Xiao, G., Lamba, V., Liang, F. (2017) Inference of Genetic Networks from Next Generation Sequencing Data. Biometrics.
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