Summary and printing methods for objects of the class drift_dm, resulting
from a call to drift_dm.
Usage
# S3 method for class 'drift_dm'
summary(object, ...)
# S3 method for class 'summary.drift_dm'
print(x, ..., round_digits = drift_dm_default_rounding())Value
summary.drift_dm() returns a list of class summary.drift_dm (see the
Details section summarizing each entry of this list).
print.summary.drift_dm() returns invisibly the summary.drift_dm object.
Details
The summary.drift_dm() function constructs a summary list with detailed
information about the drift_dm object, including:
class: The class type of the
drift_dmobject.summary_flex_prms: A summary of the flex_prms object in the model (see summary.flex_prms).
prms_solve: Parameters used for solving the model (see prms_solve).
solver: The solver used for model fitting.
obs_data: A summary table of observed response time data, if available, by response type (upper/lower boundary responses). Includes sample size, mean, and quantiles.
fit_stats: Fit statistics, if available, including log-likelihood, AIC, and BIC values.
The print.summary.drift_dm() function displays this summary in a formatted
way.
Examples
# get a pre-built model for demonstration purpose
a_model <- dmc_dm(t_max = 1.5, dx = .0025, dt = .0025)
sum_obj <- summary(a_model)
print(sum_obj, round_digits = 2)
#> Class(es): dmc_dm, drift_dm
#>
#> Current Parameter Matrix:
#> muc b non_dec sd_non_dec tau a A alpha
#> comp 4 0.6 0.3 0.02 0.04 2 0.1 4
#> incomp 4 0.6 0.3 0.02 0.04 2 -0.1 4
#>
#> Unique Parameters:
#> muc b non_dec sd_non_dec tau a A alpha
#> comp 1 2 3 4 5 0 6 7
#> incomp 1 2 3 4 5 0 d 7
#>
#> Special Dependencies:
#> A ~ incomp == -(A ~ comp)
#>
#> Custom Parameters:
#> peak_l
#> comp 0.04
#> incomp 0.04
#>
#> Observed Data:
#> NULL
#>
#> Fit Indices:
#> NULL
#> -------
#> Solver: kfe
#> Settings: sigma=1, t_max=1.5, dt=0.002, dx=0.002, nt=600, nx=800
# more information is provided when we add data to the model
obs_data(a_model) <- dmc_synth_data # (data set comes with dRiftDM)
summary(a_model)
#> Class(es): dmc_dm, drift_dm
#>
#> Current Parameter Matrix:
#> muc b non_dec sd_non_dec tau a A alpha
#> comp 4 0.6 0.3 0.02 0.04 2 0.1 4
#> incomp 4 0.6 0.3 0.02 0.04 2 -0.1 4
#>
#> Unique Parameters:
#> muc b non_dec sd_non_dec tau a A alpha
#> comp 1 2 3 4 5 0 6 7
#> incomp 1 2 3 4 5 0 d 7
#>
#> Special Dependencies:
#> A ~ incomp == -(A ~ comp)
#>
#> Custom Parameters:
#> peak_l
#> comp 0.04
#> incomp 0.04
#>
#> Observed Data:
#> min. 1st qu. median mean 3rd qu. max. n
#> corr comp 0.331 0.436 0.479 0.507 0.549 1.075 292
#> corr incomp 0.313 0.474 0.528 0.543 0.592 0.879 268
#> err comp 0.428 0.458 0.526 0.564 0.621 0.871 8
#> err incomp 0.302 0.398 0.452 0.458 0.498 0.771 32
#>
#> Fit Indices:
#> NULL
#> -------
#> Solver: kfe
#> Settings: sigma=1, t_max=1.5, dt=0.002, dx=0.002, nt=600, nx=800
# finally: fit indices are provided once we evaluate the model
a_model <- re_evaluate_model(a_model)
summary(a_model)
#> Class(es): dmc_dm, drift_dm
#>
#> Current Parameter Matrix:
#> muc b non_dec sd_non_dec tau a A alpha
#> comp 4 0.6 0.3 0.02 0.04 2 0.1 4
#> incomp 4 0.6 0.3 0.02 0.04 2 -0.1 4
#>
#> Unique Parameters:
#> muc b non_dec sd_non_dec tau a A alpha
#> comp 1 2 3 4 5 0 6 7
#> incomp 1 2 3 4 5 0 d 7
#>
#> Special Dependencies:
#> A ~ incomp == -(A ~ comp)
#>
#> Custom Parameters:
#> peak_l
#> comp 0.04
#> incomp 0.04
#>
#> Observed Data:
#> min. 1st qu. median mean 3rd qu. max. n
#> corr comp 0.331 0.436 0.479 0.507 0.549 1.075 292
#> corr incomp 0.313 0.474 0.528 0.543 0.592 0.879 268
#> err comp 0.428 0.458 0.526 0.564 0.621 0.871 8
#> err incomp 0.302 0.398 0.452 0.458 0.498 0.771 32
#>
#> Fit Indices:
#> Log_Like AIC BIC
#> 137.152 -260.304 -229.526
#> -------
#> Solver: kfe
#> Settings: sigma=1, t_max=1.5, dt=0.002, dx=0.002, nt=600, nx=800