Description Usage Arguments Value Note Author(s) References See Also Examples

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

1 2 3 |

`object` |
a fitted model object of class |

`...` |
for QIC and QIC |

`typeR` |
logical, whether to calculate QIC(R). QIC(R) is
based on quasi-likelihood of a working correlation |

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*.

This implementation is based partly on (revised) code from packages yags (R-Forge) and ape.

Kamil Bartoń

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

Methods exist for
`gee`

(package gee),
`geeglm`

(geepack),
`geem`

(geeM),
`wgee`

(wgeesel, the package's `QIC.gee`

function is used),
and
`yags`

(yags on R-Forge).
There is also a `QIC`

function in packages MESS and geepack,
returning some extra information (such as CIC and QICc). `yags`

and
`compar.gee`

from package ape both provide QIC values.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
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")
# QIC function is also defined in 'geepack' but is returns a vector[6], so
# cannot be used as 'rank'. Either use `MuMIn::QIC` syntax or make a wrapper
# around `geepack::QIC`
QIC <- MuMIn::QIC
## Not run:
QIC <- function(x) geepack::QIC(x)[1]
## End(Not run)
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")
))
## End(Not run)
``` |

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