feat.rank.re: Feature Ranking with Resampling Method

In mt: Metabolomics Data Analysis Toolbox

Feature Ranking with Resampling Method

Description

Feature selection with resampling method.

Usage

  feat.rank.re(x,y,method=,pars = valipars(),tr.idx=NULL,...)

Arguments

x	A matrix or data frame containing the explanatory variables.
y	A factor specifying the class for each observation.
method	Feature selection method to be used. For each method used in this function, the output must be a list including two components, fs.rank (rank scores of features) and fs.order(feature orders in descending order).
pars	A list of resampling scheme method such as Leave-one-out cross-validation, Cross-validation, Bootstrap and Randomised validation (holdout). See valipars for details.
tr.idx	User defined index of training samples. Can be generated by trainind.
...	Additional parameters to method.

Value

A list with components:

method	Feature selection method used.
fs.rank	A vector of final feature ranking list.
fs.order	A vector of final feature order from best to worst.
rank.list	Feature rank lists of all computation.
order.list	Feature order lists of all computation.
pars	Resampling parameters.
tr.idx	Index of training samples.
all	All results come from re-sampling.

Author(s)

Wanchang Lin

See Also

valipars, feat.freq, frankvali

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 <- which(cls==1 | cls==2)
x   <- dat[ind,,drop=FALSE] 
y   <- cls[ind, drop=TRUE]   

## feature selection
pars   <- valipars(sampling="boot",niter=2,nreps=5)
tr.idx <- trainind(y,pars=pars)

z      <- feat.rank.re(x,y,method="fs.auc",pars = pars)
names(z)
               

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