prostate.train

<p>Gene expression training data of 12600 genes from 52 patients with 
prostate tumors and 50 normal specimens from the microarray study of 
Singh et al. (2002).</p>

datasets

Variable selection techniques are essential tools for model
selection and estimation in high-dimensional statistical models. Through this
publicly available package, we provide a unified environment to carry out
variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all
of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards
model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).

Yang Feng

Sure Independence Screening

Jianqing Fan

Diego Franco Saldana

Yichao Wu

Richard Samworth

prostate.train function

<p>A data frame with 102 observations on 12600 variables.</p>

Format

Gene expression Prostate Cancer training data set from Singh et al. (2002) — prostate.train

Gene expression Prostate Cancer training data set from Singh et al. (2002)

prostate.train: Gene expression Prostate Cancer training data set from Singh et al. (2002)

Description

Usage

Arguments

Format

References