Write a training set including the top-ranked G variables from a sorted matrix to file

Description

This function takes a matrix of rank-ordered variables and writes a training set containing the top G variables in the matrix to file.

Usage

printTopGenes (retMatrix, numGlist=c(10, 30, 50, 100, 500, 1000, ncol(trainData)), trainData, myPrefix="sorted_topCoxphGenes_")

Arguments

Details

This function is called by iterateBMAsurv.train.predict.assess. It is meant to be used in conjunction with singleGeneCoxph, as the retMatrix argument is returned by singleGeneCoxph.

Value

A file or files consisting of the training data sorted in descending order by the top-ranked G variables (one file for each G in numGList).

References

Annest, A., Yeung, K.Y., Bumgarner, R.E., and Raftery, A.E. (2008). Iterative Bayesian Model Averaging for Survival Analysis. Manuscript in Progress.

Raftery, A.E. (1995). Bayesian model selection in social research (with Discussion). Sociological Methodology 1995 (Peter V. Marsden, ed.), pp. 111-196, Cambridge, Mass.: Blackwells.

Volinsky, C., Madigan, D., Raftery, A., and Kronmal, R. (1997) Bayesian Model Averaging in Proprtional Hazard Models: Assessing the Risk of a Stroke. Applied Statistics 46: 433-448.

Yeung, K.Y., Bumgarner, R.E. and Raftery, A.E. (2005) Bayesian Model Averaging: Development of an improved multi-class, gene selection and classification tool for microarray data. Bioinformatics 21: 2394-2402.

See Also

iterateBMAsurv.train.predict.assess, singleGeneCoxph, trainData, trainSurv, trainCens,

Examples

library(BMA)
library(iterativeBMAsurv)
data(trainData)
data(trainSurv)
data(trainCens)

## Start by ranking and sorting the genes; in this case we use the Cox Proportional Hazards Model
sorted.genes <- singleGeneCoxph(trainData, trainSurv, trainCens)

## Write top 100 genes to file
sorted.top.genes <- printTopGenes(retMatrix=sorted.genes, 100, trainData)

## The file, 'sorted_topCoxphGenes_100', is now in the working R directory.

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(iterativeBMAsurv)
Loading required package: BMA
Loading required package: survival
Loading required package: leaps
Loading required package: robustbase

Attaching package: 'robustbase'

The following object is masked from 'package:survival':

    heart

Loading required package: inline
Loading required package: rrcov
Scalable Robust Estimators with High Breakdown Point (version 1.3-11)

Loading required package: splines
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/iterativeBMAsurv/printTopGenes.Rd_%03d_medium.png", width=480, height=480)
> ### Name: printTopGenes
> ### Title: Write a training set including the top-ranked G variables from a
> ###   sorted matrix to file
> ### Aliases: printTopGenes
> ### Keywords: univar print
> 
> ### ** Examples
> 
> library(BMA)
> library(iterativeBMAsurv)
> data(trainData)
> data(trainSurv)
> data(trainCens)
> 
> ## Start by ranking and sorting the genes; in this case we use the Cox Proportional Hazards Model
> sorted.genes <- singleGeneCoxph(trainData, trainSurv, trainCens)
> 
> ## Write top 100 genes to file
> sorted.top.genes <- printTopGenes(retMatrix=sorted.genes, 100, trainData)
> 
> ## The file, 'sorted_topCoxphGenes_100', is now in the working R directory.
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>