R: B-spline Sieve Maximum Likelihood Estimation

bssmle {intccr}

R Documentation

B-spline Sieve Maximum Likelihood Estimation

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

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

`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`

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

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

Giorgos Bakoyannis, gbakogia@iu.edu

[Package intccr version 3.0.4 Index]