Description Usage Arguments Details Value Author(s) See Also Examples
Wrapper for performing feature ranking method on multiple subsets of the original
data. At each iteration, only the training samples defined in tr.idx
(or optionally pars
only) are used to rank the variables.
Features rank, order and saliency indicators calculated on the whole data are
also given in the output. As for accest
this function allows the use of multiple processors
as long as the cluster has been set up with the snow package. Data input can be
in the form of data
matrix + class
vector, following the classic formula type or
derived from dat.sel1
.
1 2 3 4 5 6 7 8 9 10 | feat.rank.re(...)
## Default S3 method:
feat.rank.re(x,y,method,pars = valipars(),tr.idx=NULL,clmpi=NULL, seed=NULL, ...)
## S3 method for class 'formula'
feat.rank.re(formula, data = NULL, ...)
## S3 method for class 'dlist'
feat.rank.re(dlist, method, pars = NULL, tr.idx = NULL, ...)
|
formula |
A formula of the form |
x |
A matrix or data frame containing the explanatory variables. |
dlist |
A matrix or data frame containing the explanatory variables if no formula is given as the principal argument. |
data |
Data frame from which variables specified in |
y |
A factor specifying the class for each observation. |
method |
Feature ranking method to be used.
See |
pars |
A list of resampling scheme or validation method such as Leave-one-out cross-validation,
Cross-validation, Bootstrap and Randomised validation (holdout).
See |
tr.idx |
User defined index of training samples of type |
clmpi |
snow cluster information |
seed |
Seed. |
... |
Additional parameters to be passed to |
The structure of the feat.rank.re
object is as follows:
Feature ranking method used.
A vector of final feature ranking scores.
A vector of final feature order from best to worst.
A vector of means of statistics or measurements in all computation.
Feature rank list of all computation.
Feature order list of all computation.
Resampling parameters.
Index of training samples.
Condensed form of the resampling strategy for output purposes.
Condensed form of the classification task.
Feature ranking object originated from the overall dataset.
feat.rank.re
object.
David Enot dle@aber.ac.uk
valipars
, ftrank.agg
, fs.techniques
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## load abr1
data(abr1)
y <- factor(abr1$fact$class)
x <- preproc(abr1$pos , y=y, method=c("log10","TICnorm"),add=1)[,110:500]
## Select classes 1 and 2
pars <- valipars(sampling="boot",niter=2,nreps=5)
dat <- dat.sel1(x, y, pwise=c("1","2"),mclass=NULL,pars=pars)
## multiple rankings using AUC
z <- feat.rank.re(dat[[1]],method="fs.auc")
## print content of z
names(z)
|
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