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Fit a Time-Varying Parameter with Radial Basis Functions

Source: R/calibrator_arg_constructors.R
mp_rbf.Rd

Pass the output of this function to the tv argument of mp_tmb_calibrator to model time variation of a parameter with flexible radial basis functions.

Usage

mp_rbf(
  tv,
  dimension,
  initial_weights,
  seed,
  prior_sd = 1,
  fit_prior_sd = TRUE,
  sparse_tol = 0.01
)

Arguments

tv

String giving the name of the parameter.

dimension

Number of bases.

initial_weights

Optional vector with dimensions elements. These are the parameters that are fitted and determine how tv varies with time.

seed

Optional random seed to use to generate the initial_weights if they are not provided.

prior_sd

Prior standard deviation default value for radial basis function coefficients, defaults to 1.

fit_prior_sd

Should the prior sd be be fitted.

sparse_tol

Tolerance below which radial basis function outputs are set exactly to zero. Small values are more accurate but slower. Lack of accuracy can be visually apparent as jumps in graphs of the time-varying parameter.