fs.snr: Feature Selection Using Signal-to-Noise Ratio (SNR)

In mt: Metabolomics Data Analysis Toolbox

Feature Selection Using Signal-to-Noise Ratio (SNR)

Description

Feature selection using signal-to-noise ratio (SNR).

Usage

  fs.snr(x,y,...)

Arguments

x	A data frame or matrix of data set.
y	A factor or vector of class.
...	Arguments to pass(current ignored).

Value

A list with components:

fs.rank	A vector of feature ranking scores.
fs.order	A vector of feature order from best to worst.
stats	A vector of measurements.

Note

This function is for two-class problem only.

Author(s)

Wanchang Lin

Examples

## prepare data set
data(abr1)
cls <- factor(abr1$fact$class)
dat <- abr1$pos
## dat <- abr1$pos[,110:1930]

## fill zeros with NAs
dat <- mv.zene(dat)

## missing values summary
mv <- mv.stats(dat, grp=cls) 
mv    ## View the missing value pattern

## filter missing value variables
## dim(dat)
dat <- dat[,mv$mv.var < 0.15]
## dim(dat)

## fill NAs with mean
dat <- mv.fill(dat,method="mean")

## log transformation
dat <- preproc(dat, method="log10")

## select class "1" and "2" for feature ranking
ind <- grepl("1|2", cls)
mat <- dat[ind,,drop=FALSE] 
mat <- as.matrix(mat)
grp <- cls[ind, drop=TRUE]   

## apply SNR method for feature selection/ranking
res <- fs.snr(mat,grp)
names(res)

mt documentation built on June 22, 2024, 12:24 p.m.