| curelps.object {blapsr} | R Documentation |
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. |
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 oswaldo_gressani@hotmail.fr.