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JMbayes (version 0.8-85)

JMbayesObject: Fitted JMbayes Object

Description

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

Arguments

Value

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

mcmc

a list with the MCMC samples for each parameter (except from the random effects if control argument keepRE is FALSE).

postMeans

a list with posterior means.

postModes

a list with posterior modes calculated using kernel desnisty estimation.

postVarsRE

a list with the posterior variance-covariance matrix for the random effects of each subject.

StErr

a list with posterior standard errors.

EffectiveSize

a list with effective sample sizes.

StDev

a list with posterior standard deviations.

CIs

a list with 95% credible intervals.

vcov

the variance-covariance matrix of the model's parameters based.

pD

the pD value.

DIC

the deviance information criterion value.

CPO

the conditional predictive ordinate value.

LPML

the log pseudo marginal likelihood value.

time

the time used to fit the model.

scales

a list with scaling constants in the Metropolis algorithm.

Covs

a list with the covariance matrices of the proposals in the Metropolis algorithm.

acceptRates

a list of acceptance rates.

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

a list of data frames with the data used to fit the models.

Terms

a list of terms objects for the various parts of the joint model.

Funs

a list of functions used for the various parts of the joint model.

Forms

a list of formulas for the two submodels.

timeVar

the value of the timeVar argument

control

the value of the control argument.

densLongCheck

a logical indicating whether a scale parameter is required in the longitudinal submodel.

param

the value of the param argument.

priors

a list with the specification of the prior distributions for the model's parameters. This has the same components as the priors argument of the jointModelBayes function.

baseHaz

the value of the baseHaz argument.

df.RE

the value of the df.RE argument.

call

the matched call.

See Also

jointModelBayes