dmcFitSubjectDE {DMCfun}

dmcFitSubjectDE

Description

Fit theoretical data generated from dmcSim to observed data by minimizing the root-mean-square error (RMSE) between a weighted combination of the CAF and CDF functions using the R-package DEoptim. Alternative cost functions include squared percentage error ("SPE"), and g-squared statistic ("GS").

Usage

dmcFitSubjectDE(
  resOb,
  nTrl = 1e+05,
  minVals = list(),
  maxVals = list(),
  fixedFit = list(),
  freeCombined = list(),
  nCAF = 5,
  nDelta = 19,
  pDelta = vector(),
  tDelta = 1,
  deltaErrors = FALSE,
  costFunction = "RMSE",
  spDist = 1,
  drOnset = 0,
  drDist = 0,
  drShape = 3,
  drLim = c(0.1, 0.7),
  bndsRate = 0,
  bndsSaturation = 0,
  rtMax = 5000,
  subjects = c(),
  deControl = list(),
  numCores = 2
)

Arguments

resOb

Observed data (see flankerData and simonTask for data format)

nTrl

The number of trials to use within dmcSim.

minVals

Minimum values for the to-be estimated parameters. This is a list with values specified individually for amp, tau, drc, bnds, resMean, resSD, aaShape, spShape, sigm (e.g., minVals = list(amp = 10, tau = 5, drc = 0.1, bnds = 20, resMean = 200, resSD = 5, aaShape = 1, spShape = 2, spBias = -20, sigm = 1, bndsRate=0, bndsSaturation=0)).

maxVals

Maximum values for the to-be estimated parameters. This is a list with values specified individually for amp, tau, drc, bnds, resMean, resSD, aaShape, spShape, sigm (e.g., maxVals = list(amp = 40, tau = 300, drc = 1.0, bnds = 150, bndsRate=1, bndsSaturation=500, resMean = 800, resSD = 100, aaShape = 3, spShape = 4, spBias = 20, sigm = 10))

fixedFit

Fix parameter to starting value. This is a list with bool values specified individually for amp, tau, drc, bnds, resMean, resSD, aaShape, spShape, sigm (e.g., fixedFit = list(amp = F, tau = F, drc = F, bnds = F, bndsRate=T, bndsSaturation=T, resMean = F, resSD = F, aaShape = F, spShape = F, spBias = T, sigm = T, bndsRate=T, bndsSaturation=T)) NB. Value if fixed at midpoint between minVals and maxVals.

freeCombined

If fitting 2+ datasets at once, which parameters are allowed to vary between both fits (default = all parameters fixed between the two fits e.g. parameter = F). This is a list with bool values specified individually for amp, tau, drc, bnds, resMean, resSD, aaShape, spShape, spBias, sigm (e.g., freeCombined = list(amp = F, tau = F, drc = F, bnds = F, bndsRate=F, bndsSaturation=F, resMean = F, resSD = F, aaShape = F, spShape = F, spBias = F, sigm = F))

nCAF

The number of CAF bins.

nDelta

The number of delta bins.

pDelta

An alternative option to nDelta (tDelta = 1 only) by directly specifying required percentile values (vector of values 0-100)

tDelta

The type of delta calculation (1=direct percentiles points, 2=percentile bounds (tile) averaging)

deltaErrors

TRUE/FALSE Calculate delta bins for error trials

costFunction

The cost function to minimise: root mean square error ("RMSE": default), squared percentage error ("SPE"), or likelihood-ratio chi-square statistic ("GS")

spDist

The starting point distribution (0 = constant, 1 = beta, 2 = uniform)

drOnset

The starting point of controlled drift rate (i.e., "target" information) relative to automatic ("distractor" incormation) (> 0 ms)

drDist

The drift rate (dr) distribution type (0 = constant, 1 = beta, 2 = uniform)

drShape

The drift rate (dr) shape parameter

drLim

The drift rate (dr) range

bndsRate

0 (default) = fixed bnds

bndsSaturation

Collapsing bounds saturation

rtMax

The limit on simulated RT (decision + non-decisional components)

subjects

NULL (aggregated data across all subjects) or integer for subject number

deControl

Additional control parameters passed to DEoptim (see DEoptim.control)

numCores

Number of cores to use

Value

dmcFitSubjectDE returns a list of objects of class "dmcfit"

Examples

# Code below can exceed CRAN check time limit, hence donttest
# Example 1: Flanker data from Ulrich et al. (2015)
fit <- dmcFitSubjectDE(flankerData, nTrl = 1000, subjects = c(1, 2), deControl = list(itermax=30))
plot(fit, flankerData, subject = 1)
plot(fit, flankerData, subject = 2)
summary(fit)