sl_stderr | R Documentation |
This will help understand risk estimates of learners in SL, similar to CV.SL.
sl_stderr(sl, y, obsWeights = rep(1, length(y)))
sl |
SuperLearner result object |
y |
Outcome vector |
obsWeights |
Observation weights |
Vector of the standard errors of the risk estimate for each learner in the SL object.
Dudoit, S., & van der Laan, M. J. (2005). Asymptotics of cross-validated risk estimation in estimator selection and performance assessment. Statistical Methodology, 2(2), 131-154.
Polley EC, van der Laan MJ (2010) Super Learner in Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Paper 226. http://biostats.bepress.com/ucbbiostat/paper266/
van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007) Super Learner. Statistical Applications of Genetics and Molecular Biology, 6, article 25. http://www.degruyter.com/view/j/sagmb.2007.6.issue-1/sagmb.2007.6.1.1309/sagmb.2007.6.1.1309.xml
plot.SuperLearner
summary.CV.SuperLearner
library(SuperLearner)
library(ck37r)
data(Boston, package = "MASS")
set.seed(1)
sl = SuperLearner(Boston$medv, subset(Boston, select = -medv),
family = gaussian(), cvControl = list(V = 2),
SL.library = c("SL.mean", "SL.glm"))
sl
sl_stderr(sl, y = Boston$medv)
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