bssmle function

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)

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$\le 1$

Returns

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

Details

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

Author(s)

Giorgos Bakoyannis, gbakogia@iu.edu

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