bssmle: B-spline Sieve Maximum Likelihood Estimation

In intccr: Semiparametric Competing Risks Regression under Interval Censoring

B-spline Sieve Maximum Likelihood Estimation

Description

Routine that performs B-spline sieve maximum likelihood estimation with linear and nonlinear inequality/equality constraints

Usage

bssmle(formula, data, alpha, k = 1)

Arguments

formula	a formula object relating survival object Surv2(v, u, event) to a set of covariates
data	a data frame that includes the variables named in the formula argument
alpha	α = (α1, α2) contains parameters that define the link functions from class of generalized odds-rate transformation models. The components α1 and α2 should both be ≥ 0. If α1 = 0, the user assumes the proportional subdistribution hazards model or the Fine-Gray model for the cause of failure 1. If α2 = 1, the user assumes the proportional odds model for the cause of failure 2.
k	a parameter that controls the number of knots in the B-spline with 0.5 ≤ k ≤ 1

Details

The function bssmle performs B-spline sieve maximum likelihood estimation.

Value

The function bssmle returns a list of components:

beta	a vector of the estimated coefficients for the B-splines
varnames	a vector containing variable names
alpha	a vector of the link function parameters
loglikelihood	a loglikelihood of the fitted model
convergence	an indicator of convegence
tms	a vector of the minimum and maximum observation times
Z	a set of covariates
Tv	a vector of v
Tu	a vector of u
Bv	a list containing the B-splines basis functions evaluated at v
Bu	a list containing the B-splines basis functions evaluated at v
dBv	a list containing the first derivative of the B-splines basis functions evaluated at v
dBu	a list containing the first derivative of the B-splines basis functions evaluated at u
dmat	a matrix of event indicator functions

Author(s)

Giorgos Bakoyannis, [e-mail]

Jun Park, [e-mail]

intccr documentation built on May 10, 2022, 9:05 a.m.