Description Usage Arguments Value Author(s) References Examples

Calculates the logarithmic, quadratic/Brier and spherical scoring rules based on generalized estimation equations.

1 2 |

`object` |
an object inheriting from class |

`rule` |
character string indicating the type of scoring rule to be used. |

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

`K` |
numeric scalar indicating the number of folds used in the cross-validation procedure. |

`M` |
numeric scalar denoting how many times the split of the data in |

`seed` |
numeric scalre providing the seed used in the procedure. |

`return_data` |
logical; if |

A list or a data.frame with elements or (extra) columns the values of the logarithmic, quadratic and spherical scoring rules calculated based on the GEE object.

Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl

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

Liang, K.Y. and Zeger, S.L. (1986). Longitudinal data analysis using generalized linear models. *Biometrika* **73**, 13-22. doi:10.1093/biomet/73.1.13

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
library("geepack")
library("lattice")
pbc2$serBilirD <- as.numeric(pbc2$serBilir > 1.2)
fm1 <- geeglm(serBilirD ~ year, family = binomial(), data = pbc2,
id = id, corstr = "exchangeable")
fm2 <- geeglm(serBilirD ~ year * drug, family = binomial(), data = pbc2,
id = id, corstr = "exchangeable")
plot_data <- cv_gee(fm1, return_data = TRUE, M = 5)
plot_data$model_year <- plot_data$.score
plot_data$model_year_drug <- unlist(cv_gee(fm2, M = 5))
xyplot(model_year + model_year_drug ~ year | .rule, data = plot_data,
type = "smooth", auto.key = TRUE, layout = c(3, 1),
scales = list(y = list(relation = "free")),
xlab = "Follow-up time (years)", ylab = "Scoring Rules")
``` |

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