logitord {repeated} | R Documentation |

`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.

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)

`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. |

A list of class `logitord`

is returned.

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

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

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]