Description Usage Arguments Value Author(s) References See Also Examples

Calculates the logarithmic, quadratic/Brier and spherical based on a fitted mixed model for categorical data.

1 2 | ```
scoring_rules(object, newdata, newdata2 = NULL, max_count = 2000,
return_newdata = FALSE)
``` |

`object` |
an object inheriting from class |

`newdata` |
a data.frame based on which to estimate the random effect and calculate predictions. It should contain the response variable. |

`newdata2` |
a data.frame based on which to estimate the random effect and calculate predictions. It should contain the response variable. |

`max_count` |
numeric scalar denoting the maximum count up to which to calculate probabilities; this is relevant for count response data. |

`return_newdata` |
logical; if |

A data.frame with (extra) columns the values of the logarithmic, quadratic and spherical
scoring rules calculated based on the fitted model and the observed responses in
`newdata`

or `newdata2`

.

Dimitris Rizopoulos [email protected]

Carvalho, A. (2016). An overview of applications of proper scoring rules.
*Decision Analysis* **13**, 223–242. doi:10.1287/deca.2016.0337

1 2 3 |

GLMMadaptive documentation built on May 2, 2019, 2:51 p.m.

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