geeglm {geepack} | R Documentation |
The geeglm function fits generalized estimating equations using the 'geese.fit' function of the 'geepack' package for doing the actual computations. geeglm has a syntax similar to glm and returns an object similar to a glm object. An important feature of geeglm, is that an anova method exists for these models.
geeglm(formula, family = gaussian, data=parent.frame(), weights, subset, na.action, start = NULL, etastart, mustart, offset, control = geese.control(...), method = "glm.fit", x = FALSE, y = TRUE, contrasts = NULL, id, waves=NULL, zcor=NULL, corstr = "independence", scale.fix = FALSE, scale.value =1, std.err="san.se", ...)
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 |
na.action |
See corresponding documentation to glm |
start |
See corresponding documentation to glm |
etastart |
See corresponding documentation to glm |
mustart |
See corresponding documentation to glm |
offset |
See corresponding documentation to glm |
control |
See corresponding documentation to glm |
method |
See corresponding documentation to glm |
x |
See corresponding documentation to glm |
y |
See corresponding documentation to glm |
contrasts |
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 are assumed to be sorted so that observations on a cluster are contiguous rows for all entities in the formula. |
waves |
Wariable specifying the ordering of repeated mesurements on the same unit. Also used in connection with missing values. See examples below. |
zcor |
Used for entering a user defined working correlation structure. |
corstr |
a character string specifying the correlation structure. The following are permitted: '"independence"', '"exchangeable"', '"ar1"', '"unstructured"' and '"userdefined"' |
scale.fix |
a logical variable; if true, the scale parameter is fixed at the value of 'scale.value'. |
scale.value |
numeric variable giving the value to which the scale parameter should be fixed; used only if 'scale.fix == TRUE'. |
std.err |
Type of standard error to be calculated. Defualt 'san.se' is the usual robust estimate. Other options are 'jack': if approximate jackknife variance estimate should be computed. 'j1s': if 1-step jackknife variance estimate should be computed. 'fij': logical indicating if fully iterated jackknife variance estimate should be computed. |
... |
further arguments passed to or from other methods. |
~Describe the value returned If it is a LIST, use
comp1 |
Description of 'comp1' |
comp2 |
Description of 'comp2' |
...
geeglm has not been thoroughly tested. Please report bugs.
See the documentation for the 'geese' function for additional information.
Søren Højsgaard, sorenh@agrsci.dk
Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. Biometrika, *73* 13-22.
Prentice, R.L. and Zhao, L.P. (1991). Estimating equations for parameters in means and covariances of multivariate discrete and continuous responses. Biometrics, *47* 825-839.
data(dietox) dietox$Cu <- as.factor(dietox$Cu) mf <- formula(Weight~Cu*(Time+I(Time^2)+I(Time^3))) gee1 <- geeglm(mf, data=dietox, id=Pig, family=poisson("identity"),corstr="ar1") gee1 summary(gee1) mf2 <- formula(Weight~Cu*Time+I(Time^2)+I(Time^3)) gee2 <- geeglm(mf2, data=dietox, id=Pig, family=poisson("identity"),corstr="ar1") anova(gee2)