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Summary and Printing for fits_agg_dm Objects

Source: R/formatting_fits_agg_dm.R
summary.fits_agg_dm.Rd

Methods for summarizing and printing objects of the class fits_agg_dm, which contain model fits based on aggregated data across participants.

Usage

# S3 method for class 'summary.fits_agg_dm'
print(x, ..., just_header = FALSE, round_digits = drift_dm_default_rounding())

# S3 method for class 'fits_agg_dm'
summary(object, ..., select_unique = FALSE)

Arguments

x

an object of class summary.fits_agg_dm.

...

additional arguments (currently unused).

just_header

logical, if TRUE only print the header information without details. Default is FALSE.

round_digits

an integer, specifying the number of decimal places for rounding in the printed summary. Default is 3.

object

an object of class fits_agg_dm, typically generated by a call to estimate_dm.

select_unique

logical, passed to coef.drift_dm().

Value

summary.fits_agg_dm() returns a list of class summary.fits_agg_dm (see Details for its structure).

print.summary.fits_agg_dm() returns the input object invisibly.

Details

The summary.fits_agg_dm function creates a structured summary of a fits_agg_dm object, containing:

  • summary_drift_dm_obj: A list with information about the underlying drift diffusion model (as returned by summary.drift_dm()).

  • prms: Parameter estimates obtained from the model fit. This is equivalent to a call to coef.drift_dm() on the stored model object.

  • obs_data: A list providing the number of individual participants and the average number of trials per condition across participants.

The print.summary.fits_agg_dm function formats and prints the above summary in a human-readable form.

See also

summary.drift_dm, coef.drift_dm

Examples

# Load example fit object
fits_agg <- get_example_fits("fits_agg")
sum_obj <- summary(fits_agg)
print(sum_obj, round_digits = 2)
#> Fit approach: aggregated - classical
#> Fitted model type: ratcliff_dm, drift_dm
#> Optimizer: nmkb
#> Convergence: TRUE
#> N Individuals: 3 
#> Average Trial Numbers:
#>  100 trials null
#> 
#> Parameters:
#>       muc    b non_dec
#> null 2.89 0.53    0.29
#> 
#> Cost Function: rmse
#> 
#> Fit Indices:
#>     Log_Like Neg_Log_Like          AIC          BIC       RMSE_s      RMSE_ms 
#>           NA           NA           NA           NA         0.01         9.38 
#> 
#> -------
#> Deriving PDFS:
#>   solver: kfe
#>   values: sigma=1, t_max=3, dt=0.005, dx=0.005, nt=600, nx=400