geeglm: Fit Generalized Estimating Equations (GEE)

In geepack: Generalized Estimating Equation Package

Fit Generalized Estimating Equations (GEE)

Description

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.

Usage

geeglm(
  formula,
  family = gaussian,
  data = parent.frame(),
  weights,
  subset,
  na.action,
  start = NULL,
  etastart,
  mustart,
  offset,
  control = geese.control(...),
  method = "glm.fit",
  contrasts = NULL,
  id,
  waves = NULL,
  zcor = NULL,
  corstr = "independence",
  scale.fix = FALSE,
  scale.value = 1,
  std.err = "san.se",
  ...
)

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
na.action	No action is taken. Indeed geeglm only works on complete data.
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
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 each cluster appear as contiguous rows in data. If data is not sorted this way, the function will not identify the clusters correctly. If data is not sorted this way, a warning will be issued. Please consult the package vignette for details.
waves	Wariable specifying the ordering of repeated mesurements on the same unit. Also used in connection with missing values. Please consult the package vignette for details.
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.

Details

In the case of corstr="fixed" one must provide the zcor vector if the clusters have unequal sizes. Clusters with size one must not be represented in zcor.

Value

An object of type 'geeglm'

Warning

Use "unstructured" correlation structure only with great care. (It may cause R to crash).

Note

See the documentation for the 'geese' function for additional information. geeglm only works for complete data. Thus if there are NA's in data you can specify data=na.omit(mydata).

Author(s)

Søren Højsgaard, [e-mail]

References

Halekoh, U.; Højsgaard, S. and Yan, J (2006) The R Package geepack for Generalized Estimating Equations. Journal of Statistical Software, 15, 2, 1-11"

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.

See Also

geese, glm, anova.geeglm

Examples

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
coef(gee1)
vcov(gee1)
summary(gee1)
coef(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)

# Notice the difference here: Clusters of observations must
# appear as chunks in data. 

set.seed(1)
chick1 <- ChickWeight
chick2 <- chick1[sample(nrow(chick1)),]
chick3 <- chick2[order(chick2$Chick),]

fit1 <- geeglm(weight~Time, id=Chick, data=chick1)
fit2 <- geeglm(weight~Time, id=Chick, data=chick2)
fit3 <- geeglm(weight~Time, id=Chick, data=chick3)

vcov(fit1)
vcov(fit2)
vcov(fit3)

geepack documentation built on Sept. 23, 2024, 5:09 p.m.