separation.detection: separation.detection

In ChangchangXu-LTRI/Mixcure: Maximum (penalized) likelihood estimation of Weibull mixture cure model

separation.detection

Description

A function for identifying whether or not separation has occurred under MC model.

Usage

separation.detection(fit, nsteps = 30, pl)

Arguments

fit	mixcure object as the result of mixcure.penal.est function fit.
pl	if TRUE, uses FT-PL; otherwise uses the usual likelihood for the model.
nsteps	Starting from iterlim = 1, the mixcure.penal.est is refitted for iterlim = 2, iterlim = 3, . . . , maxit = nsteps. Default value is 30

Details

Identifies separated cases for object returned from mixcure.penal.est(), by refitting the model. At each iteration the maximum number of allowed IWLS iterations is fixed starting from 1 to nsteps. For each value of iterlim (under mixcure.penal.est function), the estimated asymptotic standard errors are divided to the corresponding ones resulted for iterlim=1. Based on the results in Lesaffre & Albert (1989), if the sequence of ratios in any column of the resultant matrix diverges, then separation occurs and the maximum likelihood estimate for the corresponding parameter has value minus or plus infinity.

Value

A matrix of dimension nsteps by length of number of parameters, that contains the ratios of the estimated asymptotic standard errors.

Author(s)

Changchang Xu

References

Lesaffre (1989)

Examples

data(ANNbcBMdat1)
# Fit the MC model using maximum likelihoodlizards.
mc.mle1 <- mixcure.penal.est(Surv(Time, CENS == 1) ~ Her2,data=ANNbcBMdat1,init=c(1,-0.1,-10,1,1), pl=F)
separation.detection(mc.mle1, pl=F, nsteps =30)

ChangchangXu-LTRI/Mixcure documentation built on April 22, 2022, 3:33 p.m.