Access/Replace the Cost Function Label and Access the Cost Function Value

Functions to access/replace the cost function label of a dRiftDM object and to access the current cost function value. The cost function label codes which cost function is used during estimation (e.g., the negative log-likelihood). The cost function value indicates the current value of the cost function given the current set of parameters and the data.

Usage

cost_function(object, ...) <- value

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

cost_function(object, ...)

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

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

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

cost_value(object, ...)

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

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

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

Arguments

object

an object of type drift_dm, fits_ids_dm, or fits_agg_dm (see estimate_dm()).

...

additional arguments passed down to update_stats_agg() when setting the cost function label.

value

a character string, providing the cost function label (options are "neg_log_like" or "rmse")

eval_model

logical, indicating if the model should be re-evaluated or not when updating the conditions (see re_evaluate_model). Default is FALSE.

Value

• cost_function() returns a single character string, specifying the used cost function

• cost_function<-() returns the model object with the updated cost function.

• cost_value() returns a single numeric if object is of type drift_dm or fits_agg_dm. If there is no data attached to an object of type drift_dm, the function returns NULL. If object is of type fits_ids_dm, the function returns a data.frame with all cost values across participants.

See also

drift_dm(), re_evaluate_model()

Examples

# get a pre-built model for demonstration purpose
a_model <- ratcliff_dm(obs_data = ratcliff_synth_data)
cost_function(a_model)
#> [1] "neg_log_like"
cost_value(a_model)
#> [1] -193.7039

# switch the default cost function to rmse
cost_function(a_model) <- "rmse"
out <- estimate_dm(a_model, verbose = 0, messaging = FALSE)
# -> the model was estimated using the RMSE statistic