Ordinal Random Effects Models with Dropouts
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=T, random.out.slope=!is.null(tvcov),
random.drop.int=T, 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.
|
Description
logitord
fits an longitudinal ordinal model in discrete time to
outcomes and a logistic model to the probability of dropping out using
a common random effect for the two.Value
A list of class logitord
is returned.Author(s)
T.R. Ten Have and J.K. LindseyReferences
Ten Have, T, Kunselman, A.R., Pulkstenis, E.P. and Landis, J.R.
(1998) Biometrics 54, 367𤭯, 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))