if (FALSE) {
# Binary outcome example adapted from SuperLearner examples
set.seed(1)
N <- 200
X <- matrix(rnorm(N*10), N, 10)
X <- as.data.frame(X)
Y <- rbinom(N, 1, plogis(.2*X[, 1] + .1*X[, 2] - .2*X[, 3] +
.1*X[, 3]*X[, 4] - .2*abs(X[, 4])))
SL.library <- c("SL.glmnet", "SL.glm", "SL.knn", "SL.gam", "SL.mean")
# least squares loss function
set.seed(1) # for reproducibility
cvfit_nnls <- CV.SuperLearner(Y = Y, X = X, V = 10, SL.library = SL.library,
verbose = TRUE, method = "method.NNLS", family = binomial())
cvfit_nnls$coef
# SL.glmnet_All SL.glm_All SL.knn_All SL.gam_All SL.mean_All
# 1 0.0000000 0.00000000 0.000000000 0.4143862 0.5856138
# 2 0.0000000 0.00000000 0.304802397 0.3047478 0.3904498
# 3 0.0000000 0.00000000 0.002897533 0.5544075 0.4426950
# 4 0.0000000 0.20322642 0.000000000 0.1121891 0.6845845
# 5 0.1743973 0.00000000 0.032471026 0.3580624 0.4350693
# 6 0.0000000 0.00000000 0.099881535 0.3662309 0.5338876
# 7 0.0000000 0.00000000 0.234876082 0.2942472 0.4708767
# 8 0.0000000 0.06424676 0.113988158 0.5600208 0.2617443
# 9 0.0000000 0.00000000 0.338030342 0.2762604 0.3857093
# 10 0.3022442 0.00000000 0.294226204 0.1394534 0.2640762
# negative log binomial likelihood loss function
cvfit_nnloglik <- recombineCVSL(cvfit_nnls, method = "method.NNloglik")
cvfit_nnloglik$coef
# SL.glmnet_All SL.glm_All SL.knn_All SL.gam_All SL.mean_All
# 1 0.0000000 0.0000000 0.00000000 0.5974799 0.40252010
# 2 0.0000000 0.0000000 0.31177345 0.6882266 0.00000000
# 3 0.0000000 0.0000000 0.01377469 0.8544238 0.13180152
# 4 0.0000000 0.1644188 0.00000000 0.2387919 0.59678930
# 5 0.2142254 0.0000000 0.00000000 0.3729426 0.41283197
# 6 0.0000000 0.0000000 0.00000000 0.5847150 0.41528502
# 7 0.0000000 0.0000000 0.47538172 0.5080311 0.01658722
# 8 0.0000000 0.0000000 0.00000000 1.0000000 0.00000000
# 9 0.0000000 0.0000000 0.45384961 0.2923480 0.25380243
# 10 0.3977816 0.0000000 0.27927906 0.1606384 0.16230097
# If we use the same seed as the original `cvfit_nnls`, then
# the recombineCVSL and CV.SuperLearner results will be identical
# however, the recombineCVSL version will be much faster since
# it doesn't have to re-fit all the base learners, V times each.
set.seed(1)
cvfit_nnloglik2 <- CV.SuperLearner(Y = Y, X = X, V = 10, SL.library = SL.library,
verbose = TRUE, method = "method.NNloglik", family = binomial())
cvfit_nnloglik2$coef
# SL.glmnet_All SL.glm_All SL.knn_All SL.gam_All SL.mean_All
# 1 0.0000000 0.0000000 0.00000000 0.5974799 0.40252010
# 2 0.0000000 0.0000000 0.31177345 0.6882266 0.00000000
# 3 0.0000000 0.0000000 0.01377469 0.8544238 0.13180152
# 4 0.0000000 0.1644188 0.00000000 0.2387919 0.59678930
# 5 0.2142254 0.0000000 0.00000000 0.3729426 0.41283197
# 6 0.0000000 0.0000000 0.00000000 0.5847150 0.41528502
# 7 0.0000000 0.0000000 0.47538172 0.5080311 0.01658722
# 8 0.0000000 0.0000000 0.00000000 1.0000000 0.00000000
# 9 0.0000000 0.0000000 0.45384961 0.2923480 0.25380243
# 10 0.3977816 0.0000000 0.27927906 0.1606384 0.16230097
}
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