A simple backwards selection, a.k.a. recursive feature elimination (RFE), algorithm

`rfe(x, ...)`# S3 method for default
rfe(x, y, sizes = 2^(2:4),
metric = ifelse(is.factor(y), "Accuracy", "RMSE"),
maximize = ifelse(metric == "RMSE", FALSE, TRUE),
rfeControl = rfeControl(), ...)

rfeIter(x, y, testX, testY, sizes, rfeControl = rfeControl(),
label = "", seeds = NA, ...)

# S3 method for rfe
update(object, x, y, size, ...)

x

a matrix or data frame of predictors for model training. This object must have unique column names.

…

options to pass to the model fitting function (ignored in
`predict.rfe`

)

y

a vector of training set outcomes (either numeric or factor)

sizes

a numeric vector of integers corresponding to the number of features that should be retained

metric

a string that specifies what summary metric will be used to
select the optimal model. By default, possible values are "RMSE" and
"Rsquared" for regression and "Accuracy" and "Kappa" for classification. If
custom performance metrics are used (via the `functions`

argument in
`rfeControl`

, the value of `metric`

should match one of the
arguments.

maximize

a logical: should the metric be maximized or minimized?

rfeControl

a list of options, including functions for fitting and prediction. The web page http://topepo.github.io/caret/recursive-feature-elimination.html#rfe has more details and examples related to this function.

testX

a matrix or data frame of test set predictors. This must have
the same column names as `x`

testY

a vector of test set outcomes

label

an optional character string to be printed when in verbose mode.

seeds

an optional vector of integers for the size. The vector should
have length of `length(sizes)+1`

object

an object of class `rfe`

size

a single integers corresponding to the number of features that should be retained in the updated model

A list with elements

a list of size
`length(sizes) + 1`

containing the column names of the ``surviving''
predictors at each stage of selection. The first element corresponds to all
the predictors (i.e. `size = ncol(x)`

)

a data frame with columns for the test set outcome, the predicted outcome and the subset size.

More details on this function can be found at http://topepo.github.io/caret/recursive-feature-elimination.html.

This function implements backwards selection of predictors based on predictor importance ranking. The predictors are ranked and the less important ones are sequentially eliminated prior to modeling. The goal is to find a subset of predictors that can be used to produce an accurate model. The web page http://topepo.github.io/caret/recursive-feature-elimination.html#rfe has more details and examples related to this function.

`rfe`

can be used with "explicit parallelism", where different
resamples (e.g. cross-validation group) can be split up and run on multiple
machines or processors. By default, `rfe`

will use a single processor
on the host machine. As of version 4.99 of this package, the framework used
for parallel processing uses the foreach package. To run the resamples
in parallel, the code for `rfe`

does not change; prior to the call to
`rfe`

, a parallel backend is registered with foreach (see the
examples below).

`rfeIter`

is the basic algorithm while `rfe`

wraps these
operations inside of resampling. To avoid selection bias, it is better to
use the function `rfe`

than `rfeIter`

.

When updating a model, if the entire set of resamples were not saved using
`rfeControl(returnResamp = "final")`

, the existing resamples are
removed with a warning.

# NOT RUN { # } # NOT RUN { data(BloodBrain) x <- scale(bbbDescr[,-nearZeroVar(bbbDescr)]) x <- x[, -findCorrelation(cor(x), .8)] x <- as.data.frame(x) set.seed(1) lmProfile <- rfe(x, logBBB, sizes = c(2:25, 30, 35, 40, 45, 50, 55, 60, 65), rfeControl = rfeControl(functions = lmFuncs, number = 200)) set.seed(1) lmProfile2 <- rfe(x, logBBB, sizes = c(2:25, 30, 35, 40, 45, 50, 55, 60, 65), rfeControl = rfeControl(functions = lmFuncs, rerank = TRUE, number = 200)) xyplot(lmProfile$results$RMSE + lmProfile2$results$RMSE ~ lmProfile$results$Variables, type = c("g", "p", "l"), auto.key = TRUE) rfProfile <- rfe(x, logBBB, sizes = c(2, 5, 10, 20), rfeControl = rfeControl(functions = rfFuncs)) bagProfile <- rfe(x, logBBB, sizes = c(2, 5, 10, 20), rfeControl = rfeControl(functions = treebagFuncs)) set.seed(1) svmProfile <- rfe(x, logBBB, sizes = c(2, 5, 10, 20), rfeControl = rfeControl(functions = caretFuncs, number = 200), ## pass options to train() method = "svmRadial") ## classification data(mdrr) mdrrDescr <- mdrrDescr[,-nearZeroVar(mdrrDescr)] mdrrDescr <- mdrrDescr[, -findCorrelation(cor(mdrrDescr), .8)] set.seed(1) inTrain <- createDataPartition(mdrrClass, p = .75, list = FALSE)[,1] train <- mdrrDescr[ inTrain, ] test <- mdrrDescr[-inTrain, ] trainClass <- mdrrClass[ inTrain] testClass <- mdrrClass[-inTrain] set.seed(2) ldaProfile <- rfe(train, trainClass, sizes = c(1:10, 15, 30), rfeControl = rfeControl(functions = ldaFuncs, method = "cv")) plot(ldaProfile, type = c("o", "g")) postResample(predict(ldaProfile, test), testClass) # } # NOT RUN { ####################################### ## Parallel Processing Example via multicore # } # NOT RUN { library(doMC) ## Note: if the underlying model also uses foreach, the ## number of cores specified above will double (along with ## the memory requirements) registerDoMC(cores = 2) set.seed(1) lmProfile <- rfe(x, logBBB, sizes = c(2:25, 30, 35, 40, 45, 50, 55, 60, 65), rfeControl = rfeControl(functions = lmFuncs, number = 200)) # } # NOT RUN { # }