Description Usage Arguments Value Author(s) Examples

Computes the optimal weights to obtain the minimal loss function from a list of prediction matrices.

1 | ```
optimize_weights(predictionlist, outcome, FUN = trps)
``` |

`predictionlist` |
A list of R x T prediction matrices where each column sum to 1 and each row sums to |

`outcome` |
An integer vector listing the |

`FUN` |
The function used for optimizing the predictions. The default is top use rps for the rank probability score. Another option is logloss for log loss. |

Returns a numeric vector containing an optimal vector of weights that sum to 1 and that minimizes the loss function.

Claus Ekstrom ekstrom@sund.ku.dk

1 2 3 4 5 6 7 8 9 | ```
m1 <- matrix(c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, .5, .5, 0, 0, .5, .5), 4)
m1 # Prediction where certain on the top ranks
m2 <- matrix(c(.5, .5, 0, 0, .5, .5, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1), 4)
m2 # Prediction where the groups are okay
m3 <- matrix(c(.5, .5, 0, 0, .5, .5, 0, 0, 0, 0, .5, .5, 0, 0, .5, .5), 4)
m3 # Prediction where no clue about anything
m4 <- matrix(rep(1/4, 16), 4)
optimize_weights(list(m1, m2, m3, m4), 1:4)
``` |

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