sl_stderr: Calculate the SE of individual SL learners

Description Usage Arguments Value References See Also Examples

View source: R/sl_stderr.R

Description

This will help understand risk estimates of learners in SL, similar to CV.SL.

Usage

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sl_stderr(sl, Y, obsWeights = rep(1, length(Y)))

Arguments

sl

SuperLearner result object

Y

Outcome vector

obsWeights

Observation weights

Value

Vector of the standard errors of the risk estimate for each learner in the SL object.

References

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

See Also

plot.SuperLearner summary.CV.SuperLearner

Examples

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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)

ck37r documentation built on June 4, 2017, 1:02 a.m.