Description Usage Arguments Value References Examples

Returns summary statistics (mean, sd, min, max) on the distribution of the weights assigned to each learner across SuperLearner ensembles. This makes it easier to understand the stochastic nature of the SL learner weights and to see how often certain learners are used. This function may eventually be moved into the SuperLearner package.

1 2 | ```
cvsl_weights(cvsl, sort = T, nonzero = F, clean_names = T, rank = T,
digits = 5)
``` |

`cvsl` |
CV.SuperLearner result object |

`sort` |
If TRUE, sort rows (learners) in descending order by mean weight. |

`nonzero` |
Restrict to learners with a non-zero mean weight. |

`clean_names` |
Remove "SL." from the front and "_All" from the end of learner names. |

`rank` |
Adding the learner rank to the table. |

`digits` |
Number of digits to round the results. Set to NULL to disable. |

Table in data frame form with each learner's mean, sd, min, and max meta-weight in the ensemble of each learner.

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
library(SuperLearner)
library(ck37r)
data(Boston, package = "MASS")
set.seed(1, "L'Ecuyer-CMRG")
# Subset rows to speed up example computation.
row_subset = sample(nrow(Boston), 100)
Boston = Boston[row_subset, ]
X = subset(Boston, select = -chas)
cvsl = CV.SuperLearner(Boston$chas, X[, 1:2], family = binomial(),
cvControl = list(V = 2, stratifyCV = TRUE),
SL.library = c("SL.mean", "SL.glm"))
cvsl_weights(cvsl)
``` |

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

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