**cvGEE** calculates cross-validated versions of the logarithmic, quadratic and spherical scoring rules based on generalized estimating equations.

The package presumes that the GEE has been solved using the `geeglm()`

function of the **geepack**.

For

`family = gaussian()`

only the quadratic rule is available calculated as the squared prediction error; lower values indicate a better predictive ability.For

`family = binomial()`

and dichotomous outcome data the probabilities for the two categories are calculated from the Bernoulli probability mass function. For`family = binomial()`

and binomial data the probabilities for each possible response are calculated from a beta-binomial distribution with variance set equal to the variance from the corresponding quasi-likelihood behind the GEE. Likewise for`family = poisson()`

the probabilities for the number of events up to a particular maximum (set using the`max_count`

argument) are calculated using the negative binomial distribution with variance set equal to the variance from the corresponding quasi-likelihood behind the GEE. For these families all three scoring rules are available, with higher values in each rule indicating better predictive ability.

We compare a linear and a nonlinear GEE for the dichotomized version of the serum bilirubin biomarker from the PBC dataset:

```
library("geepack")
library("cvGEE")
library("splines")
library("lattice")
pbc2$serBilirD <- as.numeric(pbc2$serBilir > 1.2)
gm1 <- geeglm(serBilirD ~ year * drug,
family = binomial(), data = pbc2, id = id,
corstr = "exchangeable")
gm2 <- geeglm(serBilirD ~ ns(year, 3, Boundary.knots = c(0, 10)) * drug,
family = binomial(), data = pbc2, id = id,
corstr = "exchangeable")
plot_data <- cv_gee(gm1, return_data = TRUE)
plot_data$linear <- plot_data$.score
plot_data$non_linear <- unlist(cv_gee(gm2))
xyplot(linear + non_linear ~ 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")
```

The development version of the package can be installed from GitHub using the **devtools** package:

and with vignettes