Summary for traces_dm and traces_dm_list Objects
Source:R/formatting_traces_dm.R
summary.traces_dm.RdSummary and corresponding printing methods for traces_dm and
traces_dm_list objects, resulting from a call to
simulate_traces(). Here, traces_dm objects are entries of the
returned list.
Usage
# S3 method for class 'traces_dm'
summary(object, ...)
# S3 method for class 'summary.traces_dm'
print(x, ..., round_digits = drift_dm_default_rounding())
# S3 method for class 'traces_dm_list'
summary(object, ...)
# S3 method for class 'summary.traces_dm_list'
print(x, ..., round_digits = drift_dm_default_rounding())Value
summary.traces_dm() returns a list of class summary.traces_dm (see the
Details section summarizing each entry of this list).
summary.traces_dm_list() returns a list of class summary.traces_dm_list
(see the Details section summarizing each entry of this list).
print.summary.traces_dm() returns the summary.traces_dm object invisibly.
print.summary.traces_dm_list() returns the summary.traces_dm_list object
invisibly.
Details
The summary.traces_dm() function constructs a summary list with
information about the traces_dm object, including:
k: The number of traces in the object.
add_x: A logical, indicating whether starting values were added.
orig_model_class: The class label of the original model.
orig_prms: The parameters with which the traces were simulated (for the respective condition)
prms_solve: The solver settings with which the traces were simulated.
fpt_desc: A summary of the first passage times, including mean, standard deviation, and response probabilities for upper and lower boundaries.
The summary.traces_dm_list() function constructs a summary list with
information about the traces_dm_list object, including:
k: A numeric vector, providing the number of traces per condition.
add_x: A logical vector, indicating whether starting values were added for each condition.
orig_prms: A matrix, containing the original parameter values per condition, with which the traces were simulated.
orig_model_class: The class label of the original model
prms_solve: A matrix of solver settings per condition.
fpt_desc: A summary of the first passage times per condition, including mean, standard deviation, and response probabilities for the upper or lower boundary.
The print.summary.traces_dm() and print.summary.traces_dm_list()
functions display the summary in a formatted way.
Examples
# get a couple of traces a cross conditions
traces <- simulate_traces(dmc_dm(), k = c(5, 10))
summary(traces)
#> Starting Points Added:
#> comp incomp
#> no no
#>
#>
#> Number of Traces:
#> comp incomp
#> 5 10
#>
#>
#> Summary of First Passage Times:
#> mean sd p_corr p_err
#> comp 0.104 0.061 1 0
#> incomp 0.143 0.058 1 0
#>
#>
#> Orginal 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
#>
#> -------
#> Original Model Class(es): dmc_dm, drift_dm
#>
#> Settings:
#> sigma t_max dt dx nt nx
#> comp 1 3 0.001 0.001 3000 2000
#> incomp 1 3 0.001 0.001 3000 2000
#>
# get a single traces object
one_traces_obj <- traces[[1]]
summary(one_traces_obj)
#> Starting Points Added: no
#>
#> Number of Traces: 5
#>
#> Summary of First Passage Times:
#> mean sd p_corr p_err
#> 0.104 0.061 1.000 0.000
#>
#>
#> Orginal Parameter Values:
#> muc b non_dec sd_non_dec tau a A
#> 4.00 0.60 0.30 0.02 0.04 2.00 0.10
#> alpha
#> 4.00
#>
#> -------
#> Original Model Class(es): dmc_dm, drift_dm
#> Settings: sigma=1, t_max=3, dt=0.001, dx=0.001, nt=3000, nx=2000