R: Fit a generalized estimating equation (GEE) model with fixed...

geekin {MESS} | R Documentation

## Fit a generalized estimating equation (GEE) model with fixed additive correlation structure

### Description

The geekin function fits generalized estimating equations but where the correlation structure is given as linear function of (scaled) fixed correlation structures.

### Usage

    geekin(formula, family = gaussian, data, weights, subset, id, na.action,
      control = geepack::geese.control(...), varlist, ...)

### Arguments

`formula` | See corresponding documentation to `glm`.
`family` | See corresponding documentation to `glm`.
`data` | See corresponding documentation to `glm`.
`weights` | See corresponding documentation to `glm`.
`subset` | See corresponding documentation to `glm`.
`id` | a vector which identifies the clusters. The length of `id` should be the same as the number of observations. Data must be sorted so that observations on a cluster are contiguous rows for all entities in the formula. If not the function will give an error
`na.action` | See corresponding documentation to `glm`.
`control` | See corresponding documentation to `glm`.
`varlist` | a list containing one or more matrix or bdsmatrix objects that represent the correlation structures
`...` | further arguments passed to or from other methods.

### Details

The geekin function is essentially a wrapper function to `geeglm`. Through the varlist argument, it allows for correlation structures of the form

R = sum_i=1^k alpha_i R_i

where alpha_i are(nuisance) scale parameters that are used to scale the off-diagonal elements of the individual correlation matrices, R_i.

### Value

Returns an object of type `geeglm`.

### Author(s)

Claus Ekstrom claus@rprimer.dk

### See Also

`lmekin`, `geeglm`

### Examples

     # Get dataset
     library(kinship2)
     library(mvtnorm)
     data(minnbreast)

     breastpeda <- with(minnbreast[order(minnbreast$famid), ], pedigree(id,
                       fatherid, motherid, sex,
                       status=(cancer& !is.na(cancer)), affected=proband,
                       famid=famid))

    set.seed(10)

    nfam <- 6
    breastped <- breastpeda[1:nfam]

     # Simulate a response

    # Make dataset for lme4
    df <- lapply(1:nfam, function(xx) {
                as.data.frame(breastped[xx])
                })

    mydata <- do.call(rbind, df)
    mydata$famid <- rep(1:nfam, times=unlist(lapply(df, nrow)))

    y <- lapply(1:nfam, function(xx) {
                x <- breastped[xx]
                rmvtnorm.pedigree(1, x, h2=0.3, c2=0)
                })
    yy <- unlist(y)

    library(geepack)

    geekin(yy ~ 1, id=mydata$famid, varlist=list(2*kinship(breastped)))

    # lmekin(yy ~ 1 + (1|id), data=mydata, varlist=list(2*kinship(breastped)),method="REML")

[Package MESS version 0.5.6 Index]