if (FALSE) {
set.seed(23432)
## training set
n <- 500
p <- 50
X <- matrix(rnorm(n*p), nrow = n, ncol = p)
colnames(X) <- paste("X", 1:p, sep="")
X <- data.frame(X)
Y <- X[, 1] + sqrt(abs(X[, 2] * X[, 3])) + X[, 2] - X[, 3] + rnorm(n)
## build Library and run Super Learner
SL.library <- c("SL.glm", "SL.randomForest", "SL.gam", "SL.polymars", "SL.mean")
test <- CV.SuperLearner(Y = Y, X = X, V = 10, SL.library = SL.library,
verbose = TRUE, method = "method.NNLS")
test
summary(test)
## Look at the coefficients across folds
coef(test)
# Example with specifying cross-validation options for both
# CV.SuperLearner (cvControl) and the internal SuperLearners (innerCvControl)
test <- CV.SuperLearner(Y = Y, X = X, SL.library = SL.library,
cvControl = list(V = 10, shuffle = FALSE),
innerCvControl = list(list(V = 5)),
verbose = TRUE, method = "method.NNLS")
## examples with snow
library(parallel)
cl <- makeCluster(2, type = "PSOCK") # can use different types here
clusterSetRNGStream(cl, iseed = 2343)
testSNOW <- CV.SuperLearner(Y = Y, X = X, SL.library = SL.library, method = "method.NNLS",
parallel = cl)
summary(testSNOW)
stopCluster(cl)
}
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