# cvGEE v0.3-0

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## Cross-Validated Predictions from GEE

Calculates predictions from generalized estimating equations and internally cross-validates them using the logarithmic, quadratic and spherical proper scoring rules; Kung-Yee Liang and Scott L. Zeger (1986) <doi:10.1093/biomet/73.1.13>.

## Readme

# cvGEE: Cross-Validated Predictions from GEE

## Description

**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.

## Basic Use

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")
```

## Installation

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

```
devtools::install_github("drizopoulos/cvGEE")
```

and with vignettes

```
devtools::install_github("drizopoulos/cvGEE", build_vignettes = TRUE)
```

## Functions in cvGEE

Name | Description | |

aids | Didanosine versus Zalcitabine in HIV Patients | |

cvGEE | Proper Scoring Rules for Generalized Estimating Equations | |

pbc2 | Mayo Clinic Primary Biliary Cirrhosis Data | |

cv_gee | Proper Scoring Rules for Generalized Estimating Equations | |

No Results! |

## Vignettes of cvGEE

Name | ||

Scoring_Rules_GEE.Rmd | ||

No Results! |

## Last month downloads

## Details

Date | 2019-07-20 |

BugReports | https://github.com/drizopoulos/cvGEE/issues |

Encoding | UTF-8 |

LazyLoad | yes |

LazyData | yes |

License | GPL (>= 3) |

URL | https://drizopoulos.github.io/cvGEE/, https://github.com/drizopoulos/cvGEE |

VignetteBuilder | knitr |

RoxygenNote | 6.1.1 |

NeedsCompilation | no |

Packaged | 2019-07-20 18:16:39 UTC; drizo |

Repository | CRAN |

Date/Publication | 2019-07-23 14:52:05 UTC |

#### Include our badge in your README

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```