cv_gee {cvGEE}

Proper Scoring Rules for Generalized Estimating Equations

Description

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

Usage

cv_gee(object, rule = c("all", "quadratic", "logarithmic", "spherical"), 
  max_count = 500, K = 5L, M = 10L, seed = 1L, return_data = FALSE)

Arguments

object

an object inheriting from class "geeglm" of the geepack.

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 K folds will be performed. The reported scoring rules values are the average over the M replicates.

seed

numeric scalre providing the seed used in the procedure.

return_data

logical; if TRUE the values of the scoring rules are ruturned as extra columns of the data behind object.

Value

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.

Author(s)

Dimitris Rizopoulos [e-mail]

References

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

Examples

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")