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Fit the 2d GLAM without covariates

Source: R/GLAM_2d_no_covariates.R
GLAM_2d_no_covariates.Rd

GLAM_2d_no_covariates() fits a GLAM for the hazard with two time scales, without covariates.

Usage

GLAM_2d_no_covariates(
  R,
  Y,
  Bu,
  Bs,
  Wprior = NULL,
  P,
  ridge = 0,
  control_algorithm = list(maxiter = 20, conv_crit = 1e-05, verbose = FALSE)
)

Arguments

R

A matrix of exposure times of dimension nu by ns.

Y

A matrix of event counts of dimension nu by ns.

Bu

A matrix of B-splines for the u time scale of dimension nu by cu.

Bs

A matrix of B-splines for the s time scale of dimension ns by cs.

Wprior

An optional matrix of a-priori weights.

P

The penalty matrix of dimension cucs by cucs.

ridge

A ridge penalty parameter: default is 0.

control_algorithm

A list with optional values for the parameters of the iterative processes:

  • maxiter The maximum number of iteration for the IWSL algorithm. Default is 20.

  • conv_crit The convergence criteria, expressed as difference between estimates at iteration i and i+1. Default is 1e-5.

  • verbose A Boolean. Default is FALSE. If TRUE monitors the iteration process.

Value

A list with the following elements:

  • Alpha The matrix of estimated P-splines coefficients of dimension cu by cs.

  • Cov_alpha The variance-covariance matrix of the Alpha coefficients, of dimension cucs by cucs.

  • Eta0 The matrix of values of the baseline linear predictor (log-hazard) of dimension nu by ns.

  • H The hat-matrix.

  • deviance The deviance.

  • ed The effective dimension of the model.

  • aic The value of the AIC.

  • bic The value of the BIC.

  • Bbases a list with the B-spline bases Bu and Bs.