summary and corresponding printing methods for objects of class drift_dm,
created by 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 details
for the entries).
print.summary.drift_dm() returns invisibly the summary.drift_dm object.
Details
summary.drift_dm() constructs a summary list with information about the
drift_dm object. The returned list has class summary.drift_dm and can
include the following entries:
class: Class vector of the
drift_dmobject.summary_flex_prms: 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: Solver used for generating model predictions.
b_coding: Boundary coding for the model (see b_coding).
obs_data: Summary table of observed response time data, if available, by response type (upper/lower boundary). rows correspond to upper first then lower responses; row names are prefixed by the boundary names from
b_coding. columns (all lower-case) are:min,1st qu.,median,mean,3rd qu.,max, andn.cost_function: Name (or descriptor) of the cost function used during estimation.
fit_stats: Fit statistics, if available. we return a named atomic vector created via
unlist(unpack_obj(calc_stats(..., type = "fit_stats"))).estimate_info: Additional information about the estimation procedure.
print.summary.drift_dm() displays this summary in a formatted way.
Examples
# get a pre-built model for demonstration
a_model <- dmc_dm()
sum_obj <- summary(a_model)
print(sum_obj, round_digits = 2)
#> Class(es) dmc_dm, drift_dm
#>
#> Parameter Values:
#> 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
#>
#> Parameter Settings:
#> 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:
#> Log_Like Neg_Log_Like AIC BIC RMSE_s RMSE_ms
#> NA NA NA NA NA NA
#>
#> -------
#> Deriving PDFS:
#> solver: kfe
#> values: sigma=1, t_max=3, dt=0.0075, dx=0.02, nt=400, nx=100
#>
#> Boundary Coding:
#> upper: corr
#> lower: err
#> expected data column: Error (corr = 0; err = 1)
# 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
#>
#> Parameter Values:
#> 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
#>
#> Parameter Settings:
#> 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 Neg_Log_Like AIC BIC RMSE_s RMSE_ms
#> 124.641 -124.641 -235.283 -204.504 0.083 83.002
#>
#> -------
#> Deriving PDFS:
#> solver: kfe
#> values: sigma=1, t_max=3, dt=0.0075, dx=0.02, nt=400, nx=100
#>
#> Boundary Coding:
#> upper: corr
#> lower: err
#> expected data column: Error (corr = 0; err = 1)
# fit indices are added once we evaluate the model
a_model <- re_evaluate_model(a_model)
summary(a_model)
#> Class(es) dmc_dm, drift_dm
#>
#> Parameter Values:
#> 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
#>
#> Parameter Settings:
#> 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 Neg_Log_Like AIC BIC RMSE_s RMSE_ms
#> 124.641 -124.641 -235.283 -204.504 0.083 83.002
#>
#> -------
#> Deriving PDFS:
#> solver: kfe
#> values: sigma=1, t_max=3, dt=0.0075, dx=0.02, nt=400, nx=100
#>
#> Boundary Coding:
#> upper: corr
#> lower: err
#> expected data column: Error (corr = 0; err = 1)