dmcFitSubject {DMCfun}

dmcFitSubject

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 optim (Nelder-Mead). Alternative cost functions include squared percentage error ("SPE"), and g-squared statistic ("GS").

Usage

dmcFitSubject(
  resOb,
  nTrl = 1e+05,
  startVals = list(),
  minVals = list(),
  maxVals = list(),
  fixedFit = list(),
  freeCombined = list(),
  fitInitialGrid = TRUE,
  fitInitialGridN = 10,
  fixedGrid = 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(),
  printInputArgs = TRUE,
  printResults = FALSE,
  optimControl = list(),
  numCores = 2
)

Arguments

resOb

Observed data (see flankerData and simonTask for data format) and the function dmcObservedData to create the required input from either an R data frame or external *.txt/*.csv files

nTrl

Number of trials to use within dmcSim.

startVals

Starting values for the to-be estimated parameters. This is a list with values specified individually for amp, tau, drc, bnds, resMean, resSD, aaShape, spShape, spBias, sigm (e.g., startVals = list(amp = 20, tau = 200, drc = 0.5, bnds = 75, resMean = 300, resSD = 30, aaShape = 2, spShape = 3, spBias = 0, sigm = 4, bndsRate=0, bndsSaturation=0)).

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, spBias, sigm (e.g., minVals = list(amp = 0, tau = 5, drc = 0.1, bnds = 20, bndsRate=0, bndsSaturation=0, resMean = 200, resSD = 5, aaShape = 1, spShape = 2, spBias = -20, sigm = 1)).

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, spBias, 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, spBias, 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)) NB. Value if fixed at startVals.

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))

fitInitialGrid

TRUE/FALSE

fitInitialGridN

10 linear steps between parameters min/max values (reduce if searching more than ~2/3 initial parameters)

fixedGrid

Fix parameter for initial grid search. This is a list with bool values specified individually for amp, tau, drc, bnds, resMean, resSD, aaShape, spShape, spBias, sigm (e.g., fixedGrid = list(amp = T, tau = F, drc = T, bnds = T, bndsRate=T, bndsSaturation=T, resMean = T, resSD = T, aaShape = T, spShape = T, spBias = T, sigm = T)). As a default, the initial gridsearch only searches the tau space.

nCAF

Number of CAF bins.

nDelta

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 (sp) 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

Collapsing bounds rate. 0 (default) = fixed bounds

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

printInputArgs

TRUE (default) /FALSE

printResults

TRUE/FALSE (default)

optimControl

Additional control parameters passed to optim (see optim details section)

numCores

Number of cores to use

Value

dmcFitSubject 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 <- dmcFitSubject(flankerData, nTrl = 1000, subjects = c(1, 2));
plot(fit, flankerData, subject = 1)
plot(fit, flankerData, subject = 2)
summary(fit)