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 |
family |
See corresponding documentation to |
data |
See corresponding documentation to |
weights |
See corresponding documentation to |
subset |
See corresponding documentation to |
id |
a vector which identifies the clusters. The length of |
na.action |
See corresponding documentation to |
control |
See corresponding documentation to |
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")