RAEN: Random Ensemble Variable Selection for High Dimensional Data

Description Usage Arguments Value Examples

View source: R/RAEN.R

Description

Perform variable selection for high dimensional data

Usage

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RAEN(
  x,
  y,
  B,
  ngrp = floor(15 * ncol(x)/nrow(x)),
  parallel = TRUE,
  family = "competing",
  ncore = 2
)

## S3 method for class 'RAEN'
predict(object, newdata, ...)

Arguments

x

the predictor matrix

y

the time and status object for survival

B

times of bootstrap

ngrp

the number of blocks to separate variables into. Default is 15*p/N, where p is the number of predictors and N is the sample size.

parallel

Logical TRUE or FALSE. Whether to use multithread computing, which can save consideratable amount of time for high dimensional data. Default is TRUE.

family

what family of data types. Default is 'competing'. Quantile regression for competing risks will be available through the developmental version on github

ncore

Number of cores used for parallel computing, if parallel=TRUE

object

the RAEN object containing the variable selection results

newdata

the predictor matrix for prediction

...

other parameters to pass

Value

a dataframe with the variable names and the regression coefficients

the linear predictor of the outcome risk

Examples

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library(RAEN)
data(toydata)
x=toydata[,-c(1:2)]
y=toydata[,1:2]
fgrp<-deCorr(x, ngrp=20)

RAEN documentation built on Feb. 22, 2021, 9:07 a.m.