calc_stats: Calculate Statistics

In dRiftDM: Estimating (Time-Dependent) Drift Diffusion Models

Calculate Statistics

Description

calc_stats provides an interface for calculating statistics/metrics on model predictions and/or observed data. Supported statistics include basic statistics on mean and standard deviation, Conditional Accuracy Functions (CAFs), Quantiles, Delta Functions, and fit statistics. Results can be aggregated across individuals.

Usage

calc_stats(object, type, ...)

## S3 method for class 'data.frame'
calc_stats(
  object,
  type,
  ...,
  conds = NULL,
  resample = FALSE,
  progress = 1,
  level = "individual",
  b_coding = NULL
)

## S3 method for class 'drift_dm'
calc_stats(object, type, ..., conds = NULL, resample = FALSE)

## S3 method for class 'fits_ids_dm'
calc_stats(
  object,
  type,
  ...,
  conds = NULL,
  resample = FALSE,
  progress = 1,
  level = "individual"
)

## S3 method for class 'fits_agg_dm'
calc_stats(
  object,
  type,
  ...,
  conds = NULL,
  resample = FALSE,
  progress = 1,
  level = "group",
  messaging = TRUE
)

## S3 method for class 'stats_dm'
print(
  x,
  ...,
  round_digits = NULL,
  print_rows = NULL,
  some = NULL,
  show_header = NULL,
  show_note = NULL
)

## S3 method for class 'stats_dm_list'
print(x, ...)

Arguments

object	an object for which statistics are calculated. This can be a data.frame of observed data, a drift_dm object, a fits_ids_dm object, or a fits_agg_dm object (see estimate_dm()).
type	a character vector, specifying the statistics to calculate. Supported values include "basic_stats", "cafs", "quantiles", "delta_funs", "densities", and "fit_stats".
...	additional arguments passed to the respective method and the underlying calculation functions (see Details for mandatory arguments).
conds	optional character vector specifying conditions to include. Conditions must match those found in the object.
resample	logical. If TRUE, then data is (re-)sampled to create an uncertainty estimate for the requested summary statistic. See Details for more information. Default is FALSE. Note that resampling does not work with type = "fit_stats".
progress	integer, indicating if information about the progress should be displayed. 0 -> no information, 1 -> a progress bar. Default is 1.
level	a single character string, indicating at which "level" the statistic should be calculated. Options are "group" or "individual". If "individual", the returned stats_dm object contains an "ID" column.
b_coding	a list for boundary coding (see b_coding). Only relevant when object is a data.frame. For other object types, the b_coding of the object is used.
messaging	logical, if FALSE, no message is provided.
x	an object of type stats_dm or stats_dm_list, as returned by the function calc_stats().
round_digits	integer, controls the number of digits shown. Default is 3.
print_rows	integer, controls the number of rows shown.
some	logical. If TRUE, a subset of randomly sampled rows is shown.
show_header	logical. If TRUE, a header specifying the type of statistic will be displayed.
show_note	logical. If TRUE, a footnote is displayed indicating that the underlying data.frame can be accessed as usual.

Details

calc_stats is a generic function to handle the calculation of different statistics/metrics for the supported object types. Per default, it returns the requested statistics/metrics.

List of Supported Statistics

Basic Statistics

With "basic statistics", we refer to a summary of the mean and standard deviation of response times, including a proportion of response choices.

Conditional Accuracy Function (CAFs)

CAFs are a way to quantify response accuracy against speed. To calculate CAFs, RTs (whether correct or incorrect) are first binned and then the percent correct responses per bin is calculated.

When calculating model-based CAFs, a joint CDF combining both the pdf of correct and incorrect responses is calculated. Afterwards, this CDF is separated into even-spaced segments and the contribution of the pdf associated with a correct response relative to the joint CDF is calculated.

The number of bins can be controlled by passing the argument n_bins. The default is 5.

Quantiles

For observed response times, the function stats::quantile is used with default settings.

Which quantiles are calcuated can be controlled by providing the probabilites, probs, with values in [0, 1]. Default is seq(0.1, 0.9, 0.1).

Delta Functions

Delta functions calculate the difference between quantiles of two conditions against their mean:

• Delta_i = Q_{i,j} - Q_{i,k}

• Avg_i = 0.5 \cdot Q_{i,j} + 0.5 \cdot Q_{i,k}

With i indicating a quantile, and j and k two conditions.

To calculate delta functions, users have to specify:

• minuends: character vector, specifying condition(s) j. Must be in conds(drift_dm_obj).

• subtrahends: character vector, specifying condition(s) k. Must be in conds(drift_dm_obj)

• dvs: character, indicating which quantile columns to use. Default is "Quant_<u_label>". If multiple dvs are provided, then minuends and subtrahends must have the same length, and matching occurs pairwise. In this case, if only one minuend/subtrahend is specified, minuend and subtrahend are recycled to the necessary length.

• specifying probs is possible (see Quantiles)

Densities

With "densities", we refer to a summary of the distribution of observed or predicted data. For observed data, histogram values and kernel density estimates are provided. For predicted data, the model's predicted PDFs are provided.

Optional arguments are:

• discr: numeric, the band-width when calculating the histogram or the kernel density estimates. Defaults to 0.015 seconds

• t_max: numeric, the maximum time window when calculating the distribution summaries of observe data. Defaults to the longest RT (for observed data) or the maximum of the time domain of a model (which is the preferred choice, if possible). If necessary, t_max is slightly adjusted to match with discr.

• scale_mass: logical, only relevant if observed data is available. If TRUE, density masses are scaled proportional to the number of trials per condition.

Fit Statistics

Calculates the Log-Likelihood, Akaike and Bayesian Information Criteria, and root-mean squared-error statistic.

Optional arguments are:

• k: numeric, for penalizing the AIC statistic (see also stats::AIC and AIC.fits_ids_dm).

• n_bins, probs: numeric vectors, see the section on CAFs and Quantiles above

• weight_err: numeric scalar, determines how CAFs and quantiles are weighted. Default is 1.5.

Resampling

When resampling = TRUE, an uncertainty interval is provided via simulation. The default number of iterations is R = 100, which can be changed by passing the optional argument R.

If resampling is requested, the returned stats_dm object contains the column "Estimate", coding the interval. The interval width is controlled via the optional argument interval_level, a single numeric value between 0 and 1 (default: 0.95). The interpretation of this interval depends on the specific situation (see below).

Resampling at the Individual Level

If object is a drift_dm object (i.e., a single model created by drift_dm()), synthetic data are simulated under the model, and for each synthetic data set the requested statistic is calculated. The interval then reflects the range of these simulated statistics. To determine the number of trials for each synthetic data set, dRiftDM either uses the observed data attached to the model (if available) or the optional argument n_sim (passed to simulate_data()). Note that n_sim must be provided if no observed data are available, and that n_sim always has priority.

If object is a drift_dm object with attached observed data, resampling is also performed for the observed data. In this case, trials are bootstrapped, and for each bootstrap sample the requested statistic is calculated.

If object is a data.frame, fits_agg_dm, or fits_ids_dm object, resampling is performed for each individual if level = "individual". For both models and observed data, synthetic or bootstrapped data sets are generated as described above.

Resampling at the Group Level

Group-level resampling is possible only if object is a data.frame (with an "ID" column), fits_agg_dm, or fits_ids_dm object. To request this, set level = "group". Participants are then bootstrapped, and for each bootstrapped sample the aggregated statistic is calculated.

Interpretation of Intervals

For level = "group", intervals represent bootstrapped confidence intervals For level = "individual", intervals represent the variability in the statistic when data for a single participant are resampled or simulated under the model.

Note

For objects of type fits_agg_dm, which contain a mixture of group- and individual-level information, the level argument only affects resampling for the observed data. For the model itself, resampling is always performed under the fitted model, in the same way as for a drift_dm object.

Value

If type is a single character string, then a subclass of data.frame is returned, containing the respective statistic. Objects of type sum_dist will have an additional attribute storing the boundary encoding (see also b_coding). The reason for returning subclasses of data.frame is to provide custom plot() methods (e.g., plot.cafs). To get rid of the subclass label and additional attributes (i.e., to get just the plain underlying data.frame, users can use unpack_obj()).

If type contains multiple character strings (i.e., is a character vector) a subclass of list with the calculated statistics is returned. The list will be of type stats_dm_list (to easily create multiple panels using the respective plot.stats_dm_list() method).

The print methods print.stats_dm() and print.stats_dm_list() each invisibly return the supplied object x.

Note

When a model's predicted density function integrates to a value of less than drift_dm_skip_if_contr_low(), means and quantiles return the values NA. Users can alter this by explicitly passing the argument skip_if_contr_low when calling calc_stats() (e.g., calc_stats(..., skip_if_contr_low = -Inf))

Examples

# Example 1: Calculate CAFs and Quantiles from a model ---------------------
# get a model for demonstration purpose
a_model <- ssp_dm()
# and then calculate cafs and quantiles
some_stats <- calc_stats(a_model, type = c("cafs", "quantiles"))
print(some_stats)

# Example 2: Calculate a Delta Function from a data.frame ------------------
# get a data set for demonstration purpose
some_data <- ulrich_simon_data
conds(some_data) # relevant for minuends and subtrahends
some_stats <- calc_stats(
  a_model,
  type = "delta_funs",
  minuends = "incomp",
  subtrahends = "comp"
)
print(some_stats, print_rows = 5)


# Example 3: Calculate Quantiles from a fits_ids_dm object -----------------
# get an auxiliary fits_ids_dm object
all_fits <- get_example_fits("fits_ids_dm")
some_stats <- calc_stats(all_fits, type = "quantiles")
print(some_stats, print_rows = 5) # note the ID column

# one can also request that the statistics are averaged across individuals
print(
  calc_stats(all_fits, type = "quantiles", average = TRUE)
)

dRiftDM documentation built on Dec. 1, 2025, 5:08 p.m.