The Parameters for Deriving Model Predictions

Functions to get or set the "solver settings" of an object. This includes the diffusion constant and the discretization of the time and evidence space.

Usage

prms_solve(object, ...) <- value

# S3 method for class 'drift_dm'
prms_solve(object, ..., eval_model = FALSE) <- value

prms_solve(object, ...)

# S3 method for class 'drift_dm'
prms_solve(object, ...)

# S3 method for class 'fits_ids_dm'
prms_solve(object, ...)

# S3 method for class 'fits_agg_dm'
prms_solve(object, ...)

Arguments

object: an object of type drift_dm, fits_ids_dm, or fits_agg_dm (see estimate_dm()).
...: additional arguments (i.e., eval_model).
value: a named numeric vector providing new values for the prms_solve vector (see drift_dm()).
eval_model: logical, indicating if the model should be re-evaluated or not when updating the solver settings (see re_evaluate_model). Default is FALSE.

Value

For prms_solve() the vector prms_solve (see drift_dm()).

For prms_solve<-() the updated drift_dm object.

Details

prms_solve() is a generic accessor function, and prms_solve<-() is a generic replacement function. The default methods get and set the "solver settings".

It is possible to update parts of the "solver setttings" (i.e., parts of the underlying prms_solve vector). However, modifying "nx" or "nt" is not allowed! Any attempts to modify the respective entries will silently fail (no explicit error/warning etc. is thrown).

Note

There is only a replacement function for drift_dm objects. This is because replacing the solver settings after the model has been fitted (e.g., for a fits_ids_dm object) doesn't make sense.

Examples

# get some default model to demonstrate the prms_solve() functions
my_model <- ratcliff_dm()
# show the discretization and scaling of the model
prms_solve(my_model)
#>   sigma   t_max      dt      dx      nt      nx 
#> 1.0e+00 3.0e+00 7.5e-03 2.0e-02 4.0e+02 1.0e+02 
# partially modify these settings
prms_solve(my_model)[c("dx", "dt")] <- c(0.005)
prms_solve(my_model)
#> sigma t_max    dt    dx    nt    nx 
#> 1e+00 3e+00 5e-03 5e-03 6e+02 4e+02 

# accessor method also available for fits_ids_dm objects
# (see estimate_model_ids)
# get an exemplary fits_ids_dm object
fits <- get_example_fits("fits_ids_dm")
prms_solve(fits)
#> sigma t_max    dt    dx    nt    nx 
#> 1e+00 3e+00 1e-02 1e-02 3e+02 2e+02