wgee: Fit Weighted Generalized Estimating Equations (WGEE)

In wgeesel: Weighted Generalized Estimating Equations and Model Selection

Description

wgee fits weighted generalized estimating equations (WGEE) with Newton Raphson algorithm. wgee has a syntax similar to glm and returns an object similar to a glm object.

Usage

wgee(model, data, id, family, corstr, scale = NULL, mismodel = NULL, maxit=200, tol=0.001)

Arguments

model	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data	a data frame containing the variables in the model.
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.
family	a description of the error distribution and link function to be used in the model. This is a character string naming a family function. The following are permitted: "gaussian", "binomial", "poisson".
corstr	a character string specifies the working correlation structure. The following are permitted: "independence", "exchangeable", "ar1","unstructured".
scale	a numeric variable giving the value to which the scale parameter should be fixed; if NA, the scale parameter is not fixed.
mismodel	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the missingness model to be fitted.
maxit	maximum iteration number for Newton-Raphson algorithm.
tol	the tolorance for the Newton-Raphson algorithm to converge.

Details

wgee analyzes longitudinal data with missing values by weighted genralized estimating equations (WGEE), proposed by Robins, Totnizky and Zhao (1995). WGEE can handle missing at random problem. The standard error of the estimates are calculated as described in (Fitzmaurice, Laird, and Ware, 2011) and Preisser, Lohman, and Rathouz (2002).

Value

beta	covariate effect estimates
var	variance covariances estimates for beta
w_r_square	weighted R square for continuous data
mu_fit	fitted values of response
scale	scale estimates
rho	rho estimates
weight	The weight of response y
model	WGEE model structure
x	covariates in WGEE
y	response in WGEE
mis_fit	esimates of the missingness model
call	the function to be called
id	as input
data	as input
family	as input
corstr	as input

Author(s)

Zheng Li, Cong Xu and Ming Wang

References

Fitzmaurice, G.M., Laird, N.M. and Ware, J.H., 2012. Applied longitudinal analysis (Vol. 998). John Wiley & Sons.

Liang, K.Y. and Zeger, S.L., 1986. Longitudinal data analysis using generalized linear models. Biometrika, pp.13-22.

Preisser, J.S., Lohman, K.K. and Rathouz, P.J., 2002. Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random. Statistics in medicine, 21(20), pp.3035-3054.

Robins, J.M., Rotnitzky, A. and Zhao, L.P., 1995. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association, 90(429), pp.106-121.

Rubin, D.B., 1976. Inference and missing data. Biometrika, pp.581-592.

See Also

geeglm (geepack)

Examples

####Example1
data(imps)

fit <- wgee(IMPS79 ~ Drug+Sex+Time, data=imps, id=imps$ID, family="gaussian",
            corstr="exchangeable", scale=NULL, mismodel= R ~ Drug+Time)

#####Example2
data(seizure)

###reshapre the seizure data to "long" format
seiz.long <- reshape(seizure,
                     varying=list(c("base","y1", "y2", "y3", "y4")),
                     v.names="y", times=0:4, direction="long")
seiz.long <- seiz.long[order(seiz.long$id, seiz.long$time),]

###create missing value for seiz.long dataset
set.seed(12345)
obs <- exp(9+seiz.long$age*(-0.2))/(1+exp(9+seiz.long$age*(-0.2)))
R <- lapply(unique(seiz.long$id),function(x){
  idx=which(seiz.long$id==x)
  r=c()
  r[1]=1
  for(j in 2:length(idx)){
    if(r[j-1]==1){
      r[j]=rbinom(1,1,obs[idx[j]])
    }
    else r[j]=0
  }
  return(r)
})
remove_id <- which(sapply(R,sum)==1)
remove_idx <- which(seiz.long$id %in% remove_id==1)
seiz.long <- cbind(seiz.long,R=unlist(R))[-remove_idx,]
seiz.long$y_mis <- ifelse(seiz.long$R,seiz.long$y,NA)

###fit WGEE, not run ###
fit <- wgee(y_mis ~ age + trt + time, data=seiz.long, id=seiz.long$id,family="poisson",
            corstr="exchangeable",scale=NULL, mismodel= R ~ age)

wgeesel documentation built on May 2, 2019, 3:41 a.m.