jointModelObject: Fitted jointModel Object

In JM: Joint Modeling of Longitudinal and Survival Data

Fitted jointModel Object

Description

An object returned by the jointModel function, inheriting from class jointModel and representing a fitted joint model for longitudinal and time-to-event data. Objects of this class have methods for the generic functions anova, coef, fitted, fixed.effects, logLik, plot, print, random.effects, residuals, summary, and vcov.

Value

The following components must be included in a legitimate jointModel object.

coefficients	a list with the estimated coefficients. The components of this list are: betas the vector of fixed effects for the linear mixed effects model. sigma the measurement error standard deviation for the linear mixed effects model. gammas the vector of baseline covariates for the survival model. alpha the association parameter(s). Dalpha the association parameter(s) corresponding to the slope of the true trajectory. sigma.t the scale parameter for the Weibull survival model; returned only when method = "weibull-PH-GH" or method = "weibull-AFT-GH". xi the parameter of the piecewise constant baseline hazard; returned only when method = "piecewise-PH-GH". gamma.bs the coefficients of the B-splines use to approximate the baseline hazard; returned only when method = "spline-PH-GH". lambda0 a two-column numeric matrix with the first column containing the estimated baseline hazard values, and the second the unique sorted event times; returned only when method = "Cox-PH-GH". D the variance-covariance matrix of the random effects.
Hessian	the Hessian matrix evaluated at the estimated parameter values.
logLik	the log-likelihood value.
EB	a list with components: post.b the estimated random effects values. post.vb the estimated variance for the random effects estimates. Zb the estimated random effects part of the linear predictor for the longitudinal outcome (i.e., Z is the design matrix for the random effects b). Ztimeb the estimated random effects part of the linear predictor for the survival outcome (i.e., evaluated at the observed event times). Ztime2b the estimated random effects part of the linear predictor for the survival outcome (i.e., for the ith sample unit is evaluated at all event times that are less or equal to the ith observed event time); returned only when method = "Cox-PH-GH".
knots	the numeric vector of the knots positions; returned only when method = "spline-PH-GH", method = "piecewise-PH-GH" or method = "ch-Laplace".
iters	the number of iterations in the optimization algorithm.
convergence	convergence identifier: 0 corresponds to successful convergence, whereas 1 to a problem (i.e., when 1, usually more iterations are required).
n	the number of sample units.
N	the total number of repeated measurements for the longitudinal outcome.
ni	a vector with the number of repeated measurements for each sample unit.
d	a numeric vector with 0 denoting censored observation and 1 events.
id	the grouping vector for the longitudinal responses.
x	a list with the design matrices for the longitudinal and event processes.
y	a list with the response vectors for the longitudinal and event processes.
data.id	a data.frame containing the variables for the linear mixed effects model at the time of the event.
method	the value of the method argument.
termsY	the terms component of the lmeObject.
termsT	the terms component of the survObject.
formYx	the formula for the fixed effects part of the longitudinal model.
formYz	the formula for the random effects part of the longitudinal model.
formT	the formula for the survival model.
timeVar	the value of the timeVar argument
control	the value of the control argument.
parameterization	the value of the parameterization argument.
interFact	the value of the interFact argument
derivForm	the value of the derivForm argument.
lag	the value of the lag argument.
call	the matched call.

Author(s)

Dimitris Rizopoulos [e-mail]

See Also

jointModel

JM documentation built on Aug. 8, 2022, 5:09 p.m.