recombineCVSL | R Documentation |
Function to re-compute the V-fold cross-validated risk estimate for super learner using a new metalearning method. This function takes as input an existing CV.SuperLearner fit and applies the recombineSL
fit to each of the V Super Learner fits.
recombineCVSL(object, method = "method.NNloglik", verbose = FALSE,
saveAll = TRUE, parallel = "seq")
object |
Fitted object from |
method |
A list (or a function to create a list) containing details on estimating the coefficients for the super learner and the model to combine the individual algorithms in the library. See |
verbose |
logical; TRUE for printing progress during the computation (helpful for debugging). |
saveAll |
Logical; Should the entire |
parallel |
Options for parallel computation of the V-fold step. Use "seq" (the default) for sequential computation. |
The function recombineCVSL
computes the usual V-fold cross-validated risk estimate for the super learner (and all algorithms in SL.library
for comparison), using a newly specified metalearning method. The weights for each algorithm in SL.library
are re-estimated using the new metalearner, however the base learner fits are not regenerated, so this function saves a lot of computation time as opposed to using the CV.SuperLearner
function with a new method
argument. The output is identical to the output from the CV.SuperLearner
function.
An object of class CV.SuperLearner
(a list) with components:
call |
The matched call. |
AllSL |
If |
SL.predict |
The predicted values from the super learner when each particular row was part of the validation fold. |
discreteSL.predict |
The traditional cross-validated selector. Picks the algorithm with the smallest cross-validated risk (in super learner terms, gives that algorithm coefficient 1 and all others 0). |
whichDiscreteSL |
A list of length |
library.predict |
A matrix with the predicted values from each algorithm in |
coef |
A matrix with the coefficients for the super learner on each fold. The columns are the algorithms in |
folds |
A list containing the row numbers for each validation fold. |
V |
Number of folds for |
libraryNames |
A character vector with the names of the algorithms in the library. The format is 'predictionAlgorithm_screeningAlgorithm' with '_All' used to denote the prediction algorithm run on all variables in X. |
SL.library |
Returns |
method |
A list with the method functions. |
Y |
The outcome |
Erin LeDell ledell@berkeley.edu
recombineSL
## Not run:
# 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
## End(Not run)
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