An object returned by the coxlps function consists in a list
with various components related to the fit of a Cox model using the
Laplace-P-spline methodology.
A coxlps object has the following elements:
The formula of the Cox model.
Number of B-spline basis functions used for the fit.
Chosen penalty order.
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.
Sample size.
The number of events that occurred.
The standardized event times, i.e. if t denotes
the original time scale, then event.times = t / sd(t), where
sd is the standard deviation.
The upper bound of the follow-up, i.e. max(event.times).
The standard deviation of the event times in original scale.
The event indicators.
Posterior estimates of the regression coefficients. coef gives the point estimate, sd.post gives the posterior standard deviation, z is the Wald test statistic, lower .95 and upper .95 the posterior approximate 95% quantile-based credible interval.
The selected grid of penalty values.
The maximum a posteriori of the (log) penalty parameter.
The estimated B-spline coefficients.
Estimated effective degrees of freedom for each latent field variable.
The effective model dimension.
The posterior covariance matrix of the B-spline coefficients.
The matrix of covariate values.
The log-likelihood evaluated at the posterior latent field estimate.
Number of parametric coefficients in the model.
The AIC computed with the formula -2*loglik+2*p, where p is the number of parametric coefficients.
The AIC computed with the formula -2*loglik+2*ED, where ED is the effective model dimension.
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.
The BIC computed with the formula -2*loglik+ED*log(ne), where ED is the effective model dimension and ne the number of events.
Oswaldo Gressani oswaldo_gressani@hotmail.fr.
coxlps, coxlps.baseline