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Fit Generalized Estimating Equations

Description

Calculate coefficients and nuisance parameters using generalized estimating equations. Link and Variance functions can be specified by the user. Similar toglm.

Usage

geem(formula, id, waves=NULL, data = parent.frame(), family = gaussian, corstr = "independence", Mv = 1, weights = NULL, corr.mat = NULL, init.beta = NULL, init.alpha = NULL, init.phi = 1, scale.fix = FALSE, nodummy=FALSE, sandwich = TRUE,  useP = TRUE, maxit = 20, tol = 1e-05)

Arguments

Details

Users may specify functions for link and variance functions, but the functions must be vectorized functions. SeeVectorize for an easy way to vectorize functions.Vectorize should be used sparingly, however, as it can lead to fairly slow function calls. Care must be taken to ensure that convergence is possible with non-standard functions.

Offsets must be specified in the model formula, as in glm.

For the"userdefined" correlation option, the function accepts a matrix with consecutive integers.geem only looks at the upper triangle of the matrix. Any entry given as 0 will be fixed at 0. All entries given as 1 will be assumed to be the same as each other and will be assumed to be possibly different from entries with a 2, and so on.

If observations are dropped because they have a weight of 0, then the denominator for the moment estimates of the correlation matrices are calculated using the number of non-zero Pearson residuals for the correlation structuresunstructured,userdefined andm-dependent withMv>1. Therefore residuals numerically equal to 0 may cause problems in the calculation of correlation parameters.

Value

An object of class "geem" representing the fit.

Author(s)

Lee McDaniel and Nick Henderson

See Also

glm,formula,family

Examples

### Generated Negative Binomial Datageneratedata <- function(beta,alpha,gamma,X,T,n)  {     mean.vec <- exp(crossprod(t(X),beta))     y <- matrix(0,nrow=n,ncol=T)     y[,1] <- rnbinom(n,mu = mean.vec[1],size=mean.vec[1]/gamma)     for (i in 1:n)  {        for (t in 2:T)  {           innovation.mean <- mean.vec[t] - alpha*(sqrt(mean.vec[t]*mean.vec[t-1]))           I <- rnbinom(1,mu= innovation.mean,size= innovation.mean/gamma)                      first.shape <- alpha*sqrt(mean.vec[t]*mean.vec[t-1])/gamma           second.shape <- mean.vec[t-1]/gamma - first.shape           u <- rbeta(1,shape1 = first.shape,shape2=second.shape)           a <- rbinom(1,size=y[i,t-1],prob=u)           y[i,t] = a + I        }     }     longform <- c(t(y))     print(apply(y,2,mean))     simdata <- data.frame(count = longform, time = rep(X[,2],times=n),subject=rep(c(1:n),each=T))     return(simdata)}X <- cbind(rep(1,5),c(-.5,-.25,0,.25,.5))testdat <- generatedata(beta=c(1,.5),alpha=.2,gamma=.5,X=X,T=5,n=3000)far1 <- geem(count~ time, id=subject ,data = testdat, family=poisson, corstr="ar1")### Ohio respiratory data from geepackif(require(geepack)){data("ohio", package="geepack")resplogit <- geem(resp ~ age + smoke + age:smoke, id=id, data = ohio, family = binomial,corstr = "m-dep" , Mv=1)    LinkFun <- function(arg){qcauchy(arg)}  InvLink <- function(arg){pcauchy(arg)}  InvLinkDeriv <- function(arg){dcauchy(arg)}  VarFun <- function(arg){arg*(1-arg)}  FunList <- list(LinkFun, VarFun, InvLink, InvLinkDeriv)    respcauchit <- geem(resp ~ age + smoke + age:smoke, id=id, data = ohio, family = FunList,  corstr = "m-dep" , Mv=1)}### Seizure data from geepackif(require(geepack)){data("seizure", package="geepack")seiz.l <- reshape(seizure,varying=list(c("base","y1", "y2", "y3", "y4")),v.names="y", times=0:4, direction="long")seiz.l <- seiz.l[order(seiz.l$id, seiz.l$time),]seiz.l$t <- ifelse(seiz.l$time == 0, 8, 2)seiz.l$x <- ifelse(seiz.l$time == 0, 0, 1)seiz <- geem(y~ x + trt + x:trt+ offset(log(t)), id=id,data = seiz.l, family = poisson, corstr = "exchangeable")}