frank.err | R Documentation |
Get feature ranking on the training data and validate selected feature subsets by estimating their classification error rate.
frank.err(dat.tr, cl.tr, dat.te, cl.te, cl.method="svm",
fs.method="fs.auc", fs.order=NULL, fs.len="power2", ...)
dat.tr |
A data frame or matrix of training data. Feature ranking and classification model are carried on this data set. |
cl.tr |
A factor or vector of training class. |
dat.te |
A data frame or matrix of test data. Error rates are calculated on this data set. |
cl.te |
A factor or vector of test class. |
cl.method |
Classification method to be used. Any classification methods can be employed
if they have method |
fs.method |
Feature ranking method. If |
fs.order |
A vector of feature order. Default is |
fs.len |
The lengths of feature subsets used for validation. For details, see |
... |
Additional parameters to |
A list with components:
cl.method |
Classification method used. |
fs.len |
The lengths of feature subsets used for validation. |
error |
Error rate for each feature length. |
fs.method |
Feature ranking method used. |
fs.order |
Feature order vector. |
fs.rank |
Feature ranking score vector. |
Wanchang Lin
frankvali
, get.fs.len
data(abr1)
dat <- abr1$pos
x <- preproc(dat[,110:500], method="log10")
y <- factor(abr1$fact$class)
dat <- dat.sel(x, y, choices=c("1","6"))
x.1 <- dat[[1]]$dat
y.1 <- dat[[1]]$cls
idx <- sample(1:nrow(x.1), round((2/3)*nrow(x.1)), replace=FALSE)
## construct train and test data
train.dat <- x.1[idx,]
train.cl <- y.1[idx]
test.dat <- x.1[-idx,]
test.cl <- y.1[-idx]
## validate feature selection on some feature subsets
res <- frank.err(train.dat, train.cl, test.dat, test.cl,
cl.method="knn", fs.method="fs.auc",
fs.len="power2")
names(res)
## full feature order list
res$fs.order
## validation on subsets of feature order
res$error
## or first apply feature selection
fs <- fs.auc(train.dat,train.cl)
## then apply error estimation for each selected feature subset
res.1 <- frank.err(train.dat, train.cl, test.dat, test.cl,
cl.method="knn", fs.order=fs$fs.order,
fs.len="power2")
res.1$error
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