repeated (version 1.1.5)

logitord: 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 = 1e-04,
  print.level = 0
)

Value

A list of class logitord is returned.

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.

Author

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.

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))