Description Usage Arguments Details References Examples
Produce plots to check convergence of multiple-cause model
1 | check.survMCOD(fit)
|
fit |
A model fit returned by |
The plots produced show Iter+2 estimates of each parameter, where Iter is the number of iterations specified by the user in the call
to survMCOD
. The first two estimates correspond to a starting value and an improved starting value
(see Moreno-Betancur et al. 2017 for details). Healthy convergence is seen by curves showing variation in estimates across the first
three points, followed by a stabilisation of the curve around the final estimate.
Moreno-Betancur M, Sadaoui H, Piffaretti C, Rey G. Survival analysis with multiple causes of death: Extending the competing risks model. Epidemiology 2017; 28(1): 12-19.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## Example ## uncomment to run
# First we simulate data using the simMCOD function:
# datEx<-simMCOD(n=1000,xi=-1,rho=-2,phi=0,
# pgen=c(1,0,0.75,0.25,0.125,0.083),
# lambda=0.001,v=2,pUC=c(1,0.75))
# Run analysis
# fitMCOD<-survMCOD(SurvM(time=TimeEntry,time2=TimeExit,status=Status,
# weight=Pi)~X1,
# formOther=~Z1,data=datEx,UC_indicator="UC")
# Check convergence of multiple-cause analysis
# check.survMCOD(fitMCOD)
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