emmfrailty: Fitting a semiparametric multivariate mixed-Poisson model

In joolee0918/Mfrailty: Dependence multivariate random effects model via gaussian copula functions

Description

Fitting a semiparametric multivariate mixed-Poisson model via a Gaussian copula with a log-normal marginal distribution of random effects

Usage

emmfrailty(formula, data, frailty = NULL, paircop = TRUE,
  margins = "lnorm", ntype, twostage = FALSE, model = FALSE,
  model.matrix = FALSE, control = list(), init = list(),
  eventnames = NULL, parallel = FALSE, ncore = NULL, ...)

Arguments

formula	a formula object with an obect of the type Surv on the left side and the terms on the right side. Note that +strata() is not supported.
data	a 'data.frame' which has variables in 'formula'
frailty	a charhacter string specifying a group
paircop	a logical value: if TRUE, a dependent pairwise likelihood is used; otherwise, an independent mixed-Poissom model is used.
ntype	the number of types of events.
twostage	a logical value: if TRUE, two-stage procedure is used.
model	logical value: if TRUE, the model frame is returned.
model.matrix	logical value: if TRUE, the x matirx is returned.
control	an object of class specifying control options created by controlList.
init	a list specifying inital values of a vector of variances of random effects and dependence parameters. Default initial value for a variance of random effect is 1 and 0 for the dependence parameter.
eventnames	a vector of character string speficying event names
parallel	logical value: if TRUE, it supports parallel execution.
ncore	the number of cores if parallel = TRUE.

Value

an object of class emmfrailty representing the fit.

beta.coefficients	a list of each type of events of the estimated regressoin coefficients.
sig2	a vector of the estimated variances of random effects.
rho	a vector of the estimated dependence parameters from a Gaussian copula function.
ktau	a vector of the estimated Kendall's tau from a Gaussian copula function.
basecmhaz	a list of each type of events of the estiamted cumulative baseline hazards.
logLik	a value of log likelihood at the final values of the parameters.
iter	the number of iterations used.
conv	an integer code for the convergence. 0 indicates successful convergence, 1 indicates a failure to convergence, and 2 indicates the estimated dependence parameter reaches the boundary.
var	a variance-covariance matrix of the estimates.
betavar	a list of each type of the variances of the regression coefficient estimates.
sig2var	a vector of the variances of random effects variances estimates.
rhovar	a vector of the dependence parameter estimates.
varnames	a list of event names and variable names.
control	a list of control arguments used.
n	the number of sample size.
nevent	a vector of the number of each types of events.
neventtype	the number of types of events.
nknot	the number nodes used in Gaussian-quadrature.
margins	a vector of character strings specyting marginal distributions of random effects.

The object will also contain the following: two_stage, copula, model, call, optionally x, and model.

joolee0918/Mfrailty documentation built on May 7, 2019, 6:58 p.m.