R package with tools for fitting generalized linear models with clustered observations using generalized estimating equations.

To install the development version of `geeasy`

run the following commands from within R (requires that the `devtools`

package is already installed)

`devtools::install_github("annennenne/geeasy")`

`geeasy`

fits generalized linear models on data with correlated/clustered observations by use of generalized estimating equations:

```
library(geeasy)
# load data
data("respiratory")
respiratory$useid <- interaction(respiratory$center, respiratory$id)
# fit model
m <- geelm(outcome ~ treat + sex + age + baseline, data = respiratory,
id = useid, family = "binomial", corstr = "exchangeable")
```

The syntax is similar to `glm()`

, but a few additional arguments need to be specified:

`id`

: ID for identifying clusters. All observations with the same id are considered to belong to the same cluster.`corstr`

: The correlation structure that is used within each cluster. Options include “independence” (the default, corresponding to no clustering), “exchangeable” (identical pair-wise correlations between all observations within a cluster) and more, see the documentation of`geelm()`

for more details.

The package includes a selection of functions that can be used to inspect and work with GEE models. These functions can be used both with the output from `geelm()`

*and* with the output of `geeglm()`

from the `geepack`

R package.

The following functions are implemented in `geeasy`

:

`getGEE()`

`plot()`

`confint()`

`drop1()`

A few more details about the two non-standard functions, `getGEE()`

and `plot()`

are provided below. Furthermore, `geeasy`

imports the following functions from `geepack`

that are also available:

`summary()`

`print()`

`anova()`

`QIC()`

** getGEE()**:

```
# Get parameter estimates:
getGEE(m, "beta")
# Get standard errors for parameter estimates:
getGEE(m, "beta.se")
# Get estimated alpha (correlation structure parameter):
getGEE(m, "alpha")
```

This function was built to resemble the `getME()`

function from `lme4`

. Note that it can also be accessed by calling `getME()`

.

** plot()**:

```
# Plot estimates and 95% confidence intervals for one geelm model
plot(m)
# Fit a new geelm model with AR1 correlation structure AND a glm
# (corresponding to independent correlation structure)
m_ar1 <- geelm(outcome ~ treat + sex + age + baseline,
data = respiratory, id = useid,
family = "binomial", corstr = "ar1")
m_glm <- glm(outcome ~ treat + sex + age + baseline,
data = respiratory, family = "binomial")
# Plot all three models together for easy comparison
plot(m, m_ar1, m_glm)
```

Note that this plotting function can also be accessed by calling `plotEst()`

and that this function allows for any number of models to be plotted together, and it supports the model types `lm`

, `glm`

, `geelm`

, `geeglm`

, `mice`

and more.

`geelm()`

**Changing the output object:** `geelm()`

can output a `geem`

object, resembling the output of `geem()`

from the `geeM`

package:

```
m_outout_geem <- geelm(outcome ~ treat + sex + age + baseline,
data = respiratory, id = useid,
family = "binomial", corstr = "exchangeable",
output = "geem")
```

This does not change the computations performed, only the output object. This means that the output will generally *not* be identical to that of `geeM::geem()`

.

** Changing the estimation engine:** `geelm()`

allows for choosing to use `geepack`

as its computational engine as follows:

```
m_engine_geepack <- geelm(outcome ~ treat + sex + age + baseline,
data = respiratory, id = useid,
family = "binomial", corstr = "exchangeable",
engine = "geepack")
```

Note that this does **not** mean that the id variable is handled as in `geepack`

: Clusters are still constructed by assigning observations with identical values of `id`

to the same cluster.

The `geeasy`

package is based on a modified version of the `geeM`

package and the main estimation code was hence written by Lee McDaniel and Nick Henderson.

The package was modified, updated and extended by Anne Helby Petersen.

Claus Ekstrøm has contributed additional code.

Søren Højsgaard is maintainer of the `geeasy`

package.

If you find bugs or have a request for a new feature, please open an issue.