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Return the AIC of 2ts model

Source: R/get_aic_fit_2d.R
get_aic_fit_2d.Rd

get_aic_fit_2d() fits the 2ts model with or without individual level covariates and it returns the AIC of the model. See also fit2tsmodel_ucminf() and fit2ts().

Usage

get_aic_fit_2d(
  lrho,
  R,
  Y,
  Z = NULL,
  Bu,
  Bs,
  Iu,
  Is,
  Du,
  Ds,
  Wprior = NULL,
  ridge = 0,
  control_algorithm = list(maxiter = 20, conv_crit = 1e-05, verbose = FALSE, monitor_ev =
    FALSE)
)

Arguments

lrho

A vector of two elements, the initial values for \(\log_{10}(\varrho_u)\) and \(\log_{10}(\varrho_s)\).

R

A matrix (or 3d-array) of exposure times of dimension nu by ns (or nu by ns by n).

Y

A matrix (or 3d-array) of event counts of dimension nu by ns (or nu by ns by n).

Z

(optional) A regression matrix of covariates values of dimensions n by p.

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.

Iu

An identity matrix of dimension nbu by nbu.

Is

An identity matrix of dimension nbs by nbs.

Du

The difference matrix over u.

Ds

The difference matrix over s.

Wprior

An optional matrix of a-priori weights.

ridge

A ridge penalty parameter: default is 0. This is useful when, in some cases the algorithm shows convergence problems. In this case, set to a small number, for example 1e-4.

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.

  • monitor_ev A Boolean. Default is FALSE. If TRUE monitors the evaluation of the model over the log_10(rho_s) values.

Value

The aic value of the fitted model.