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Iterative Weighted Least Squares algorithm for 1ts model

Source: R/iwls_1d.R
iwls_1d.Rd

iwls_1d() fits the 1ts model with IWLS algorithm.

Usage

iwls_1d(
  r,
  y,
  Bs,
  P,
  Wprior = NULL,
  control_algorithm = list(maxiter = 20, conv_crit = 1e-05, verbose = FALSE)
)

Arguments

r

A vector of exposure times of length ns.

y

A vector of event counts of length ns.

Bs

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

P

The penalty matrix of dimension cs by cs.

Wprior

An optional vector of length ns of a-priori weights.

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 vector of estimated P-splines coefficients of length cs.

  • SE_alpha The vector of estimated Standard Errors for the alpha coefficients, of length cs.

  • H The hat-matrix.

  • Cov The full variance-covariance 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 basis Bs (this is a list for compatibility with functions in 2d).