Description Usage Arguments Value Examples

Estimate the performance of the Super Learner for predicting the optimal weighted combination via cross-validation.

1 2 3 | ```
r2_optWeight(object, Y, X, evalV = 10, return.IC = TRUE, seed = 12345,
verbose = FALSE, parallel = FALSE, n.cores = parallel::detectCores(),
...)
``` |

`object` |
A |

`Y` |
The |

`X` |
The |

`evalV` |
The number of outer cross validation folds to use to evaluate the predictive
performance of |

`return.IC` |
A |

`seed` |
Random seed to set |

`verbose` |
A |

`parallel` |
A |

`n.cores` |
A |

`...` |
Other args (not currently used) |

An cross-validated estimate of the R-squared for the optimal prediction and standard error and confidence interval.

1 2 3 4 5 6 7 8 9 10 | ```
# Example 1 -- simple fit
set.seed(1234)
X <- data.frame(x1=runif(n=100,0,5), x2=runif(n=100,0,5))
Y1 <- rnorm(100, X$x1 + X$x2, 1)
Y2 <- rnorm(100, X$x1 + X$x2, 3)
Y <- data.frame(Y1 = Y1, Y2 = Y2)
fit <- optWeight(Y = Y, X = X, SL.library = c("SL.glm","SL.mean"),
family = "gaussian",outerV = 10,
return.CV.SuperLearner = FALSE)
perf.fit <- r2_optWeight(object = fit, Y = Y, X = X, evalV = 5)
``` |

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