screen.FSelector.cfs | R Documentation |
CFS (Hall, 1999) utilizes best.first.search
to find
columns of X
correlated with Y
but not with one another (i.e.,
not redundant). CFS, combined with a search algorithm, does not rank features
and therefore does not allow for specification of either the number of
features to be chosen (k
) or the method by which they should be chosen
(selector
).
screen.FSelector.cfs(Y, X, family, verbose = FALSE, ...)
Y |
Outcome (numeric vector). See |
X |
Predictor variable(s) (data.frame or matrix). See
|
family |
Error distribution to be used in the model:
|
verbose |
Should debugging messages be printed? Default: |
... |
Currently unused. |
A logical vector with length equal to ncol(X)
.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.37.4643
data(iris)
Y <- as.numeric(iris$Species=="setosa")
X <- iris[,-which(colnames(iris)=="Species")]
screen.FSelector.cfs(Y, X, binomial())
data(mtcars)
Y <- mtcars$mpg
X <- mtcars[,-which(colnames(mtcars)=="mpg")]
screen.FSelector.cfs(Y, X, gaussian())
# based on examples in SuperLearner package
set.seed(1)
n <- 100
p <- 20
X <- matrix(rnorm(n*p), nrow = n, ncol = p)
X <- data.frame(X)
Y <- X[, 1] + sqrt(abs(X[, 2] * X[, 3])) + X[, 2] - X[, 3] + rnorm(n)
library(SuperLearner)
sl = SuperLearner(Y, X, family = gaussian(), cvControl = list(V = 2),
SL.library = list(c("SL.glm", "All"),
c("SL.glm.interaction", "screen.FSelector.cfs")))
sl
sl$whichScreen
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.