calc_stats {dRiftDM}R Documentation

Calculate Statistics

Description

calc_stats provides an interface for calculating statistics/metrics on model predictions and/or observed data. Supported statistics include 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,
  verbose = 0,
  average = FALSE,
  split_by_ID = TRUE,
  b_coding = NULL
)

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

## S3 method for class 'fits_ids_dm'
calc_stats(object, type, ..., verbose = 1, average = FALSE)

Arguments

object

an object for which statistics are calculated. This can be a data.frame of observed data, a drift_dm object, or a fits_ids_dm object (see estimate_model_ids).

type

a character vector, specifying the statistics to calculate. Supported values include "cafs", "quantiles", "delta_funs", 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.

verbose

integer, indicating if information about the progress should be displayed. 0 -> no information, 1 -> a progress bar. Default is 0.

average

logical. If TRUE, averages the statistics across individuals where applicable. Default is FALSE.

split_by_ID

logical. If TRUE, statistics are calculated separately for each individual ID in object (when object is a data.frame). Default is TRUE.

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.

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.

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:

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

To calculate delta functions, users have to specify:

Fit Statistics

Calculates the Akaike and Bayesian Information Criteria (AIC and BIC). Users can provide a k argument to penalize the AIC statistic (see stats::AIC and AIC.fits_ids_dm)

Value

If type is a single character string, then a data.frame is returned. If type contains multiple character strings (i.e., is a character vector) a list with the calculated statistics (with entries being data.frames) is returned.

Each returned data.frame has a certain class label and may store additional attributes required for the custom plot() functions. If a list is returned, then that list will have the class label list_stats_dm (to easily create multiple panels using the respective plot() method).

Examples

# Example 1: Calculate CAFs and Quantiles from a model ---------------------
# get a model for demonstration purpose
a_model <- ssp_dm(dx = .0025, dt = .0025, t_max = 2)
# and then calculate cafs and quantiles
some_stats <- calc_stats(a_model, type = c("cafs", "quantiles"))
head(some_stats$cafs)
head(some_stats$quantiles)

# 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"
)
head(some_stats)


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

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


[Package dRiftDM version 0.2.1 Index]