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.

`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]