% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cor.R, R/npc.R, R/plotcr.R
\name{rr_cif}
\alias{rr_cif}
\alias{or_cif}
\alias{random.cif}
\alias{Grandom.cif}
\alias{predictPairPlack}
\alias{cif-nonpar}
\alias{npc}
\alias{nonparcuminc}
\alias{plotcr}
\title{Non-parametric Cumulative Incidence Functions}
\usage{
rr_cif(
  cif,
  data,
  cause = NULL,
  cif2 = NULL,
  times = NULL,
  cause1 = 1,
  cause2 = 1,
  cens.code = NULL,
  cens.model = "KM",
  Nit = 40,
  detail = 0,
  clusters = NULL,
  theta = NULL,
  theta.des = NULL,
  step = 1,
  sym = 0,
  weights = NULL,
  same.cens = FALSE,
  censoring.weights = NULL,
  silent = 1,
  par.func = NULL,
  dpar.func = NULL,
  dimpar = NULL,
  score.method = "nlminb",
  entry = NULL,
  estimator = 1,
  trunkp = 1,
  admin.cens = NULL,
  ...
)

or_cif(
  cif,
  data,
  cause = NULL,
  cif2 = NULL,
  times = NULL,
  cause1 = 1,
  cause2 = 1,
  cens.code = NULL,
  cens.model = "KM",
  Nit = 40,
  detail = 0,
  clusters = NULL,
  theta = NULL,
  theta.des = NULL,
  step = 1,
  sym = 0,
  weights = NULL,
  same.cens = FALSE,
  censoring.weights = NULL,
  silent = 1,
  par.func = NULL,
  dpar.func = NULL,
  dimpar = NULL,
  score.method = "nlminb",
  entry = NULL,
  estimator = 1,
  trunkp = 1,
  admin.cens = NULL,
  ...
)

random.cif(cif, ...)

Grandom.cif(cif, ...)

predictPairPlack(cif1, cif2, status1, status2, theta)

npc(T, cause, same.cens = TRUE, sep = FALSE)

nonparcuminc(t, status, cens = 0)

plotcr(
  x,
  col,
  lty,
  legend = TRUE,
  which = 1:2,
  cause = 1:2,
  ask = prod(par("mfcol")) < length(which) && dev.interactive(),
  ...
)
}
\arguments{
\item{cif}{a cumulative incidence model object (from timereg).}

\item{data}{a data.frame with the variables.}

\item{cause}{causes to plot.}

\item{cif2}{optional second CIF model if different from first.}

\item{times}{time points for evaluation.}

\item{cause1}{cause for first coordinate.}

\item{cause2}{cause for second coordinate.}

\item{cens.code}{censoring code value.}

\item{cens.model}{censoring model type (default \code{"KM"}).}

\item{Nit}{maximum number of iterations.}

\item{detail}{level of output detail.}

\item{clusters}{cluster variable name or vector.}

\item{theta}{dependence parameter(s).}

\item{theta.des}{design matrix for theta.}

\item{step}{step size for optimization.}

\item{sym}{if 1, symmetric dependence structure.}

\item{weights}{optional weights.}

\item{same.cens}{logical; if TRUE, uses joint censoring weights.}

\item{censoring.weights}{optional pre-computed censoring weights.}

\item{silent}{verbosity level.}

\item{par.func}{optional parameter function.}

\item{dpar.func}{optional derivative of parameter function.}

\item{dimpar}{dimension of parameter vector.}

\item{score.method}{optimization method (default \code{"nlminb"}).}

\item{entry}{optional entry time variable.}

\item{estimator}{estimator type.}

\item{trunkp}{truncation probability.}

\item{admin.cens}{administrative censoring time.}

\item{...}{additional arguments.}

\item{cif1}{CIF values for subject 1 (for \code{predictPairPlack}).}

\item{status1}{status for subject 1.}

\item{status2}{status for subject 2.}

\item{T}{matrix with columns: time1, time2, status1, status2 (for \code{npc}).}

\item{sep}{logical; if TRUE, uses separate censoring models for each subject.}

\item{t}{vector of event/censoring times (for \code{nonparcuminc}).}

\item{status}{vector of status codes (for \code{nonparcuminc}).}

\item{cens}{censoring code (default 0).}

\item{x}{data matrix or competing risks object.}

\item{col}{colors for curves.}

\item{lty}{line types for curves.}

\item{legend}{logical; if TRUE, add legend.}

\item{which}{which plots to show.}

\item{ask}{logical; if TRUE, prompt before new page.}
}
\value{
For \code{npc}: matrix with columns (time, cumulative incidence).
  For \code{nonparcuminc}: matrix with time and cause-specific cumulative incidences.
}
\description{
Functions for computing and visualizing non-parametric cumulative incidence
estimates, as well as dependence measures (odds ratio, relative risk) for
bivariate competing risks data.
}
\details{
\code{npc} computes bivariate non-parametric cumulative incidence using
inverse-probability-of-censoring weights.

\code{nonparcuminc} computes univariate non-parametric cumulative incidence
for multiple causes.

\code{plotcr} plots cumulative incidence curves for competing risks using
the prodlim package.

\code{or_cif} fits an odds-ratio model for bivariate cumulative incidence.

\code{rr_cif} fits a relative-risk model for bivariate cumulative incidence.

\code{random.cif} and \code{Grandom.cif} are aliases for \code{random_cif}
and \code{Grandom_cif} (random effects CIF models).

\code{predictPairPlack} predicts pairwise joint probabilities under a
Plackett (odds-ratio) dependence model.

\code{matplot.mets.twostage} produces matrix-plots of concordance over time
from a twostage object.
}
\author{
Klaus K. Holst, Thomas Scheike
}
