Description Usage Arguments Details Value See Also

View source: R/MultiLambdaCVfun.R

Evaluates predictions by a score suitable for the corresponding response

1 2 |

`lp` |
Numerical vector. Linear predictor. |

`Y` |
Response vector: numeric, binary, factor or |

`score` |
Character. See Details. |

`model` |
Character. Any of |

`print` |
Boolean. Should the score be printed on screen. |

Several scores are allowed, depending on the type of output. For `model = "linear"`

,
`score`

equals any of `c("loglik","mse","abserror","cor","kendall","spearman")`

, denoting
CV-ed log-likelihood, mean-squared error, mean absolute error, Pearson (Kendall, Spearman) correlation with response.
For `model = "logistic"`

, `score`

equals any of `c("loglik","auc", "brier")`

, denoting
CV-ed log-likelihood, area-under-the-ROC-curve, and brier score a.k.a. MSE.
For `model = "cox"`

, `score`

equals any of `c("loglik","cindex")`

, denoting
CV-ed log-likelihood, and c-index.

Numerical value.

`CVscore`

for obtaining the cross-validated score (for given penalties), and `doubleCV`

to obtain doubly cross-validated linear predictors to which `Scoring`

can be applied to estimated predictive performance by double cross-validation. A full demo and data are available from:

https://drive.google.com/open?id=1NUfeOtN8-KZ8A2HZzveG506nBwgW64e4

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