MuMIn (version 1.15.6)

# QIC: QIC and quasi-Likelihood for GEE

## Description

Calculate quasi-likelihood under the independence model criterion (QIC) for Generalized Estimating Equations.

## Usage

```QIC(object, ..., typeR = FALSE)
QICu(object, ..., typeR = FALSE)
quasiLik(object, ...)```

## Arguments

object

a fitted model object of class `"gee"`, `"geepack"`, `"geem"` or `"yags"`.

for QIC and QIC\(_{u}\), optionally more fitted model objects.

typeR

logical, whether to calculate QIC(R). QIC(R) is based on quasi-likelihood of a working correlation \(R\) model. Defaults to `FALSE`, and QIC(I) based on independence model is returned.

## Value

If just one object is provided, returns a numeric value with the corresponding QIC; if more than one object are provided, returns a `data.frame` with rows corresponding to the objects and one column representing QIC or QIC\(_{u}\).

## References

Pan W. (2001) Akaike's Information Criterion in Generalized Estimating Equations. Biometrics 57: 120-125

Hardin J. W., Hilbe, J. M. (2003) Generalized Estimating Equations. Chapman & Hall/CRC

## See Also

Methods exist for `gee` (package gee), `geeglm` (geepack), `geem` (geeM), and `yags` (yags on R-Forge). `yags` and `compar.gee` from package ape both provide QIC values.

## Examples

Run this code
``````# NOT RUN {
data(ohio)

fm1 <- geeglm(resp ~ age * smoke, id = id, data = ohio,
family = binomial, corstr = "exchangeable", scale.fix = TRUE)
fm2 <- update(fm1, corstr = "ar1")
fm3 <- update(fm1, corstr = "unstructured")

model.sel(fm1, fm2, fm3, rank = QIC)

# }
# NOT RUN {
# same result:
dredge(fm1, m.lim = c(3, NA), rank = QIC, varying = list(
corstr = list("exchangeable", "unstructured", "ar1")
))
# }
# NOT RUN {
# }
``````

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