frailtypack (version 2.4.1)

print.multivePenal: Print a Short Summary of parameter estimates of a multivariate frailty model

Description

Prints a short summary of parameter estimates of a multivariate frailty model, or more generally an object of class 'multivePenal'.

Usage

## S3 method for class 'multivePenal':
print(x, digits = max(options()$digits - 4, 6), ...)

Arguments

x

the result of a call to the multivePenal function

digits

number of digits to print

...

other unusued arguments

Value

• Print, separately for each type of event (recurrent1, recurrent2 and terminal), the parameter estimates of the survival or hazard functions.
• n
  the number of observations used in the fit.
• n.groups
  the maximum number of groups used in the fit
• n.events
  the number of recurrent event of type 1 observed in the fit
• n.deaths
  the number of terminal events observed in the fit
• n.events2
  the number of the recurrent event of type 2 observed in the fit
• theta
  variance of the common frailty term
• alpha
  the coefficient associated with the frailty parameter terminal event hazard function
• coef
  the coefficients of the linear predictor, which multiply the columns of the model matrix.
• varH
  the variance matrix of the dependency parameters (theta, eta, alpha1, alpha2) and of the coefficients.
• varHIH
  the robust estimation of the dependency parameters and of the coefficients.
• SE(H)
  the standard error of the estimates deduced from the variance matrix of the dependency parameters and of the coefficients.
• SE(HIH)
  the standard error of the estimates deduced from the robust estimation of the variance matrix of the dependency parameters and of the coefficients.
• p
  p-value

See Also

multivePenal

Examples

# /*** multivariate frailty model ***/
data(dataMultiv)
dataMultiv$timegap<-dataMultiv$t1-dataMultiv$t0

## Calendar-time ##


MultivSpli <-multivePenal(Surv(timegap,deltaEvent1)~cluster(id)+var1+var2+terminal(deltadc)+event2(deltaEvent2),formula.terminalEvent=~var1,formula2=~var1+var2+var3,data=dataMultiv,Frailty=TRUE, recurrentAG=F,cross.validation=T,n.knots=c(4,4,4),kappa=c(1,1,1),maxit=20,hazard="Splines",initialize=T)

## print a fit
MultivSpli