coxlps.object: Object from a Cox proportional hazards fit with...

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

Object from a Cox proportional hazards fit with Laplace-P-splines.

Description

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.

Value

A coxlps object has the following elements:

formula	The formula of the Cox 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.
n	Sample size.
num.events	The number of events that occurred.
event.times	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.
tup	The upper bound of the follow-up, i.e. max(event.times).
sd.time	The standard deviation of the event times in original scale.
event.indicators	The event indicators.
regcoeff	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.
penalty.vector	The selected grid of penalty values.
vmap	The maximum a posteriori of the (log) penalty parameter.
spline.estim	The estimated B-spline coefficients.
edf	Estimated effective degrees of freedom for each latent field variable.
ED	The effective model dimension.
Covthetamix	The posterior covariance matrix of the B-spline coefficients.
X	The matrix of covariate values.
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

coxlps, coxlps.baseline

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