feat.freq {mt} | R Documentation |
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 |
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
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)
[Package mt version 2.0-1.20 Index]