AIC.tvcure: Akaike Information Criterion (AIC) of a tvcure object.

In tvcure: Additive Cure Survival Model with Time-Varying Covariates

Akaike Information Criterion (AIC) of a tvcure object.

Description

Akaike Information Criterion (AIC) for the fitted tvcure model in a tvcure.object.

Usage

## S3 method for class 'tvcure'
AIC(object, ..., k=2)

Arguments

object	A tvcure.object.
...	Other optional tvcure objects.
k	The penalty per parameter to be used. (Default: k=2 for the classical AIC).

Details

Akaike information criterion for the fitted model in a tvcure object, with a penalty calculated using the total effective degrees of freedom, -2log(L) + 2*ED.tot, smaller values being preferred during model selection.

Value

The AIC as a numeric value, computed according to the model specified in the input object.

Author(s)

Philippe Lambert [e-mail]

References

Time-varying exogenous covariates with frequently changing values in double additive cure survival model: an application to fertility. Journal of the Royal Statistical Society, Series A. <doi:10.1093/jrsssa/qnaf035>

See Also

tvcure, tvcure.object, BIC.tvcure, logEvid

Examples

require(tvcure)
## Simulated data generation
beta = c(beta0=.4, beta1=-.2, beta2=.15) ; gam = c(gam1=.2, gam2=.2)
data = simulateTVcureData(n=500, seed=123, beta=beta, gam=gam,
                          RC.dist="exponential",mu.cens=550)$rawdata
## TVcure model fitting
tau.0 = 2.7 ; lambda1.0 = c(40,15) ; lambda2.0 = c(25,70) ## Optional
model = tvcure(~z1+z2+s(x1)+s(x2), ~z3+z4+s(x3)+s(x4), data=data,
               tau.0=tau.0, lambda1.0=lambda1.0, lambda2.0=lambda2.0)
AIC(model)

tvcure documentation built on April 12, 2025, 1:58 a.m.