repeated: logitord – R documentation

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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)

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.

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))

repeated

Non-Normal Repeated Measurements Models

v1.1.2

GPL-2

Authors

Bruce Swihart [cre, aut], Jim Lindsey [aut] (Jim created this package, Bruce is maintaining the CRAN version), T.R. Ten Have [ctb, cph] (Wrote Logit_bin_nest.f90 (in binnest.f).), Richard Cook [ctb, cph] (Wrote calcs.c, calcs.h, calcs1.c, calcs1.h, calcs2.c, calcs2.h, calcs3.c, calcs3.h, calcs4.c, calcs4.h; defs.h; gaps.c, gaps.h.), Iain MacDonald [ctb, cph] (Wrote chidden.f, cphidden.f, hidden.f.), Walter Zucchini [ctb, cph] (Wrote chidden.f, cphidden.f, hidden.f.), Burton Garbow [ctb, cph] (Wrote eigen.f.), Euginia Zharichenko [ctb, cph] (Wrote logitord.f.)

Initial release