bssmle {intccr}R Documentation

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

\alpha = (\alpha1, \alpha2) contains parameters that define the link functions from class of generalized odds-rate transformation models. The components \alpha1 and \alpha2 should both be \ge 0. If \alpha1 = 0, the user assumes the proportional subdistribution hazards model or the Fine-Gray model for the cause of failure 1. If \alpha2 = 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 \le k \le 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, gbakogia@iu.edu

Jun Park, jun.park@alumni.iu.edu


[Package intccr version 3.0.4 Index]