feat.freq {mt}

Frequency and Stability of Feature Selection

Description

Frequency and stability of feature selection.

Usage

  feat.freq(x,rank.cutoff=50,freq.cutoff=0.5)

Arguments

x

A matrix or data frame of feature orders.

rank.cutoff

A numeric value for cutoff of top features.

freq.cutoff

A numeric value for cutoff of feature frequency.

Value

A list with components:

freq.all

Feature frequencies.

freq

Feature frequencies larger than freq.cutoff.

stability

Stability rate of feature ranking.

rank.cutoff

Top feature order cut-off used.

freq.cutoff

Feature frequency cut-off used.

Author(s)

Wanchang Lin

References

Davis, C. A., et al., (2006) Reliable gene signatures for microarray classification: assessment of stability and performance. Bioinformatics, vol.22, no.19, 2356 - 2363.

Michiels, S., et al., (2005) Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet, vol.365, 488 - 492.

See Also

feat.rank.re

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]   

## use resampling method of bootstrap 
pars   <- valipars(sampling="boot",niter=10, nreps=5)
z      <- feat.rank.re(mat,grp,method="fs.plsvip",pars = pars)

## compute the frequency and stability of feature selection 
freq <- feat.freq(z$order.list,rank.cutoff=50,freq.cutoff=0.5)