rmse_table.SuperLearner: Table of RMSEs from SuperLearner result

In ck37/ck37r: Chris Kennedy's R Toolkit

Table of RMSEs from SuperLearner result

Description

Calculates root mean-squared error and CIs for each learner in the SuperLearner.

Usage

## S3 method for class 'SuperLearner'
rmse_table(x, y = x$Y, sort = TRUE, version = 1, ...)

Arguments

x	SuperLearner object
y	Outcome vector, if not already added to SL object.
sort	Sort table by order of AUC.
version	1 (default) or 2. 1 averages the RMSE over folds; 2 averages the MSE and then takes the square root.
...	Any additional unused arguments, due to the prauc_table generic.

Value

Dataframe table with RMSEs.

References

Faber, N. K. M. (1999). Estimating the uncertainty in estimates of root mean square error of prediction: application to determining the size of an adequate test set in multivariate calibration. Chemometrics and Intelligent Laboratory Systems, 49(1), 79-89.

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

Examples

library(SuperLearner)
library(ck37r)

data(Boston, package = "MASS")

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

rmse_table(sl, y = Boston$chas)

ck37/ck37r documentation built on April 29, 2023, 11:42 p.m.