EDF: Compute the effective degrees freedom in a tvcure model

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

Compute the effective degrees freedom in a tvcure model

Description

Compute the effective degrees freedom in a tvcure model

Usage

EDF(model, Wood.test = FALSE, joint.computation = TRUE)

Arguments

model	A tvcure object
Wood.test	Logical indicating if P-values based on Wood's test (Biometrika 2013) of the significance of additive terms should be preferred over basic Chi-square tests. (Default: FALSE).
joint.computation	Logical indicating if variance-covariance matrices for the regression and spline parameters in the long- and short-term survival submodels should be computed jointly (TRUE) or separately (FALSE). (Default: TRUE).

Value

A list containing the effective degrees of freedom for the additive terms in the long-term (quantum) and short-term (timing) survival submodels, with the selected statistical test for significance and its P-value.

Author(s)

Philippe Lambert [e-mail]

References

Lambert, P. and Kreyenfeld, M. (2025). 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>

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)
EDF(model)

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