| coef {JM} | R Documentation |
Extracts estimated coefficients from fitted joint models.
## S3 method for class 'jointModel':
coef(object, process = c("Longitudinal", "Event"),
include.splineCoefs = FALSE, ...)
## S3 method for class 'jointModel':
fixef(object, process = c("Longitudinal", "Event"),
include.splineCoefs = FALSE, ...)
object |
an object inheriting from class jointModel. |
process |
for which model (i.e., linear mixed model or survival model) to extract the estimated coefficients. |
include.splineCoefs |
logical; if TRUE and the method argument in jointModel() is
"ph-GH" or "ch-Laplace", the estimated B-spline coefficients are included as well. |
... |
additional arguments; currently none is used. |
When process = "Event" both methods return the same output. However, for process = "Longitudinal",
the coef() method returns the subject-specific coefficients, whereas fixef() only the fixed effects.
A numeric vector or a matrix of the estimated parameters for the fitted model.
Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl
# linear mixed model fit
fitLME <- lme(sqrt(CD4) ~ obstime * drug - drug,
random = ~ 1 | patient, data = aids)
# cox model fit
fitCOX <- coxph(Surv(Time, death) ~ drug, data = aids.id, x = TRUE)
# joint model fit, under the additive log cumulative hazard model
fitJOINT <- jointModel(fitLME, fitCOX,
timeVar = "obstime", method = "ch-GH")
# fixed effects for the longitudinal process
fixef(fitJOINT)
# fixed effects + random effects estimates for the longitudinal
# process
coef(fitJOINT)
# fixed effects for the event process
fixef(fitJOINT, process = "Event")
coef(fitJOINT, process = "Event")
# fixed effects for the event process,
# include spline coefficients
fixef(fitJOINT, process = "Event", include.splineCoefs = TRUE)