ck37r (version 1.0.3)

plot.SuperLearner: Plot estimated risk and confidence interval for each learner

Description

Does not include SuperLearner or Discrete SL results as that requires CV.SuperLearner to estimate the standard errors.

Usage

# S3 method for SuperLearner
plot(x, y = x$Y, constant = qnorm(0.975), sort = T, ...)

Arguments

x

SuperLearner result object

y

Outcome vector

constant

Multiplier of the standard error for confidence interval construction.

sort

If TRUE re-orders the results by risk estimate.

...

Any remaining arguments (unused).

Value

plot object; print to display.

References

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

SuperLearner

Examples

# NOT RUN {
library(SuperLearner)
library(ck37r)

data(Boston, package = "MASS")

set.seed(1)
sl = SuperLearner(Boston$medv, subset(Boston, select = -medv),
                  family = gaussian(),
                  SL.library = c("SL.mean", "SL.glm"))

sl
plot(sl, y = Boston$chas)

# }