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compound.Cox (version 3.20)

Univariate Feature Selection and Compound Covariate for Predicting Survival

Description

Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) , statistical methods in Emura et al (2012 PLoS ONE) , Emura & Chen (2016 Stat Methods Med Res) , and Emura et al. (2019). Algorithms for generating correlated gene expressions are also available.

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Version

Install

install.packages('compound.Cox')

Monthly Downloads

294

Version

3.20

License

GPL-2

Maintainer

Takeshi Emura

Last Published

July 4th, 2020

Functions in compound.Cox (3.20)

X.pathway

Generate a matrix of gene expressions in the presence of gene pathways
CG.Clayton

Copula-graphic estimator under the Clayton copula.
PBC

Primary biliary cirrhosis (PBC) of the liver data
X.tag

Generate a matrix of gene expressions in the presence of tag genes
compound.reg

Compound shrinkage estimation under the Cox model
uni.Wald

Univariate Cox Wald test
dependCox.reg

Univariate Cox regression under dependent censoring.
compound.Cox-package

Univariate Feature Selection and Compound Covariate for Predicting Survival
CG.Gumbel

Copula-graphic estimator under the Gumbel copula.
uni.score

Univariate Cox score test
uni.selection

Univariate feature selection based on univariate significance tests
dependCox.reg.CV

Cox regression under dependent censoring.
Lung

Survival data for patients with non-small-cell lung cancer.
cindex.CV

Cross-validated c-index for measuring the predictive accuracy of a prognostic index under a copula-based dependent censoring model.