curelps.object: Object from a promotion time model fit with...

In blapsr: Bayesian Inference with Laplace Approximations and P-Splines

Object from a promotion time model fit with Laplace-P-splines.

Description

An object returned by the curelps function consists in a list with various components related to the fit of a promotion time cure model using the Laplace-P-spline methodology.

Value

A curelps object has the following elements:

formula	The formula of the promotion time cure model.
K	Number of B-spline basis functions used for the fit.
penalty.order	Chosen penalty order.
latfield.dim	The dimension of the latent field. This is equal to the sum of the number of B-spline coefficients and the number of regression parameters related to the covariates.
event.times	The observed event times.
n	Sample size.
num.events	The number of events that occurred.
tup	The upper bound of the follow up, i.e. max(event.times).
event.indicators	The event indicators.
coeff.probacure	Posterior estimates of the regression coefficients related to the cure probability (or long-term survival).
coeff.cox	Posterior estimates of the regression coefficients related to the population hazard dynamics (or short-term survival).
vmap	The maximum a posteriori of the (log-)posterior penalty parameter.
vquad	The quadrature points of (log-) posterior penalty parameters used to compute the Gaussian mixture posterior of the latent field vector.
spline.estim	The estimated B-spline coefficients.
edf	Estimated effective degrees of freedom for each latent field variable.
ED	The effective model dimension.
Covtheta.map	The posterior covariance matrix of the B-spline coefficients for a penalty fixed at its maximum posterior value.
Covlatc.map	The posterior covariance matrix of the latent field for a penalty fixed at its maximum posterior value.
X	The covariate matrix for the long-term survival part.
Z	The covariate matrix for the short-term survival part.
loglik	The log-likelihood evaluated at the posterior latent field estimate.
p	Number of parametric coefficients in the model.
AIC.p	The AIC computed with the formula -2*loglik+2*p, where p is the number of parametric coefficients.
AIC.ED	The AIC computed with the formula -2*loglik+2*ED, where ED is the effective model dimension.
BIC.p	The BIC computed with the formula -2*loglik+p*log(ne), where p is the number of parametric coefficients and ne the number of events.
BIC.ED	The BIC computed with the formula -2*loglik+ED*log(ne), where ED is the effective model dimension and ne the number of events.

Author(s)

Oswaldo Gressani [e-mail].

See Also

curelps

blapsr documentation built on Sept. 1, 2025, 5:10 p.m.