dmcFitSubject: dmcFitSubject
In DMCfun: Diffusion Model of Conflict (DMC) in Reaction Time Tasks

Description Usage Arguments Value Examples

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").

dmcFitSubject(
  resOb,
  nTrl = 1e+05,
  startVals = list(),
  minVals = list(),
  maxVals = list(),
  fixedFit = list(),
  fitInitialGrid = TRUE,
  fitInitialGridN = 10,
  fixedGrid = list(),
  nCAF = 5,
  nDelta = 19,
  pDelta = vector(),
  tDelta = 1,
  costFunction = "RMSE",
  spDist = 1,
  drDist = 0,
  drShape = 3,
  drLim = c(0.1, 0.7),
  rtMax = 5000,
  subjects = c(),
  printInputArgs = TRUE,
  printResults = FALSE,
  optimControl = list(),
  numCores = 2
)

`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 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)).
`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 = 10, tau = 5, drc = 0.1, bnds = 20, 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, 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, resMean = F, resSD = F, aaShape = F, spShape = F, spBias = T, sigm = T))
`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, 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 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)
`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)
`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
`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

dmcFitSubject returns a list of objects of class "dmcfit"

# 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)

# Example 2: Simon data from Ulrich et al. (2015)
fit <- dmcFitSubject(simonData, nTrl = 1000, subject = c(1, 2))
plot(fit, simonData, subject = 1)
plot(fit, simonData, subject = 2)
summary(fit)