logitord {repeated}R Documentation

Ordinal Random Effects Models with Dropouts

Description

logitord fits an longitudinal proportional odds model in discrete time to the ordinal outcomes and a logistic model to the probability of dropping out using a common random effect for the two.

Usage

logitord(y, id, out.ccov=NULL, drop.ccov=NULL, tvcov=NULL,
        out.tvcov=!is.null(tvcov), drop.tvcov=!is.null(tvcov),
        pout, pdrop, prand.out, prand.drop,
        random.out.int=TRUE, random.out.slope=!is.null(tvcov),
        random.drop.int=TRUE, random.drop.slope=!is.null(tvcov),
        binom.mix=5, fcalls=900, eps=0.0001, print.level=0)

Arguments

y A vector of binary or ordinal responses with levels 1 to k and 0 indicating drop-out.
id Identification number for each individual.
out.ccov A vector, matrix, or model formula of time-constant covariates for the outcome regression, with variables having the same length as y.
drop.ccov A vector, matrix, or model formula of time-constant covariates for the drop-out regression, with variables having the same length as y.
tvcov One time-varying covariate vector.
out.tvcov Include the time-varying covariate in the outcome regression.
drop.tvcov Include the time-varying covariate in the drop-out regression.
pout Initial estimates of the outcome regression coefficients, with length equal to the number of levels of the response plus the number of covariates minus one.
pdrop Initial estimates of the drop-out regression coefficients, with length equal to one plus the number of covariates.
prand.out Optional initial estimates of the outcome random parameters.
prand.drop Optional initial estimates of the drop-out random parameters.
random.out.int If TRUE, the outcome intercept is random.
random.out.slope If TRUE, the slope of the time-varying covariate is random for the outcome regression (only possible if a time-varying covariate is supplied and if out.tvcov and random.out.int are TRUE).
random.drop.int If TRUE, the drop-out intercept is random.
random.drop.slope If TRUE, the slope of the time-varying covariate is random for the drop-out regression (only possible if a time-varying covariate is supplied and if drop.tvcov and random.drop.int are TRUE).
binom.mix The total in the binomial distribution used to approximate the normal mixing distribution.
fcalls Number of function calls allowed.
eps Convergence criterion.
print.level If 1, the iterations are printed out.

Value

A list of class logitord is returned.

Author(s)

T.R. Ten Have and J.K. Lindsey

References

Ten Have, T.R., Kunselman, A.R., Pulkstenis, E.P. and Landis, J.R. (1998) Biometrics 54, 367-383, for the binary case.

See Also

nordr, ordglm.

Examples

y <- trunc(runif(20,max=4))
id <- gl(4,5)
age <- rpois(20,20)
times <- rep(1:5,4)
logitord(y, id=id, out.ccov=~age, drop.ccov=age, pout=c(1,0,0),
        pdrop=c(1,0))
logitord(y, id, tvcov=times, pout=c(1,0,0), pdrop=c(1,0))
[Package repeated version 1.0 Index]