dmcFitSubjectDE | R Documentation |
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").
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),
rtMax = 5000,
subjects = c(),
deControl = list(),
numCores = 2
)
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 |
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 |
dmcFitSubjectDE 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 <- dmcFitSubjectDE(flankerData, nTrl = 1000, subjects = c(1, 2), deControl = list(itermax=30))
plot(fit, flankerData, subject = 1)
plot(fit, flankerData, subject = 2)
summary(fit)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.