| Title: | Diffusion Model of Conflict (DMC) in Reaction Time Tasks |
|---|---|
| Description: | DMC model simulation detailed in Ulrich, R., Schroeter, H., Leuthold, H., & Birngruber, T. (2015). Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions. Cognitive Psychology, 78, 148-174. Ulrich et al. (2015) <doi:10.1016/j.cogpsych.2015.02.005>. Decision processes within choice reaction-time (CRT) tasks are often modelled using evidence accumulation models (EAMs), a variation of which is the Diffusion Decision Model (DDM, for a review, see Ratcliff & McKoon, 2008). Ulrich et al. (2015) introduced a Diffusion Model for Conflict tasks (DMC). The DMC model combines common features from within standard diffusion models with the addition of superimposed controlled and automatic activation. The DMC model is used to explain distributional reaction time (and error rate) patterns in common behavioural conflict-like tasks (e.g., Flanker task, Simon task). This R-package implements the DMC model and provides functionality to fit the model to observed data. Further details are provided in the following paper: Mackenzie, I.G., & Dudschig, C. (2021). DMCfun: An R package for fitting Diffusion Model of Conflict (DMC) to reaction time and error rate data. Methods in Psychology, 100074. <doi:10.1016/j.metip.2021.100074>. |
| Authors: | Ian G. Mackenzie [cre, aut], Carolin Dudschig [aut] |
| Maintainer: | Ian G. Mackenzie <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 4.0.3 |
| Built: | 2025-02-16 05:32:10 UTC |
| Source: | https://github.com/igmmgi/dmcfun |
Add simulated ex-gaussian reaction-time (RT) data and binary error (Error = 1, Correct = 0) data to an R DataFrame. This function can be used to create simulated data sets.
addDataDF(dat, RT = NULL, Error = NULL)addDataDF(dat, RT = NULL, Error = NULL)
dat |
DataFrame (see createDF) |
RT |
RT parameters (see rtDist) |
Error |
Error parameters (see errDist) |
DataFrame with RT (ms) and Error (bool) columns
# Example 1: default dataframe dat <- createDF() dat <- addDataDF(dat) head(dat) hist(dat$RT, 100) table(dat$Error) # Example 2: defined overall RT parameters dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = c(500, 150, 100)) boxplot(dat$RT ~ dat$Comp) table(dat$Comp, dat$Error) # Example 3: defined RT + Error parameters across conditions dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(500, 80, 100), "Comp_incomp" = c(600, 80, 140)), Error = list("Comp_comp" = 5, "Comp_incomp" = 15)) boxplot(dat$RT ~ dat$Comp) table(dat$Comp, dat$Error) # Example 4: # create dataframe with defined RT + Error parameters across different conditions dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp", "neutral"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(500, 150, 100), "Comp_neutral" = c(550, 150, 100), "Comp_incomp" = c(600, 150, 100)), Error = list("Comp_comp" = 5, "Comp_neutral" = 10, "Comp_incomp" = 15)) boxplot(dat$RT ~ dat$Comp) table(dat$Comp, dat$Error) # Example 5: # create dataframe with defined RT + Error parameters across different conditions dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Hand" = c("left", "right"), "Side" = c("left", "right"))) dat <- addDataDF(dat, RT = list("Hand:Side_left:left" = c(400, 150, 100), "Hand:Side_left:right" = c(500, 150, 100), "Hand:Side_right:left" = c(500, 150, 100), "Hand:Side_right:right" = c(400, 150, 100)), Error = list("Hand:Side_left:left" = c(5,4,2,2,1), "Hand:Side_left:right" = c(15,4,2,2,1), "Hand:Side_right:left" = c(15,7,4,2,1), "Hand:Side_right:right" = c(5,8,5,3,1))) boxplot(dat$RT ~ dat$Hand + dat$Side) table(dat$Error, dat$Hand, dat$Side)# Example 1: default dataframe dat <- createDF() dat <- addDataDF(dat) head(dat) hist(dat$RT, 100) table(dat$Error) # Example 2: defined overall RT parameters dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = c(500, 150, 100)) boxplot(dat$RT ~ dat$Comp) table(dat$Comp, dat$Error) # Example 3: defined RT + Error parameters across conditions dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(500, 80, 100), "Comp_incomp" = c(600, 80, 140)), Error = list("Comp_comp" = 5, "Comp_incomp" = 15)) boxplot(dat$RT ~ dat$Comp) table(dat$Comp, dat$Error) # Example 4: # create dataframe with defined RT + Error parameters across different conditions dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp", "neutral"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(500, 150, 100), "Comp_neutral" = c(550, 150, 100), "Comp_incomp" = c(600, 150, 100)), Error = list("Comp_comp" = 5, "Comp_neutral" = 10, "Comp_incomp" = 15)) boxplot(dat$RT ~ dat$Comp) table(dat$Comp, dat$Error) # Example 5: # create dataframe with defined RT + Error parameters across different conditions dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Hand" = c("left", "right"), "Side" = c("left", "right"))) dat <- addDataDF(dat, RT = list("Hand:Side_left:left" = c(400, 150, 100), "Hand:Side_left:right" = c(500, 150, 100), "Hand:Side_right:left" = c(500, 150, 100), "Hand:Side_right:right" = c(400, 150, 100)), Error = list("Hand:Side_left:left" = c(5,4,2,2,1), "Hand:Side_left:right" = c(15,4,2,2,1), "Hand:Side_right:left" = c(15,7,4,2,1), "Hand:Side_right:right" = c(5,8,5,3,1))) boxplot(dat$RT ~ dat$Hand + dat$Side) table(dat$Error, dat$Hand, dat$Side)
Add error bars to current plot (uses base arrows function).
addErrorBars(xpos, ypos, errorSize, arrowSize = 0.1)addErrorBars(xpos, ypos, errorSize, arrowSize = 0.1)
xpos |
x-position of data-points |
ypos |
y-position of data-points |
errorSize |
+- size of error bars |
arrowSize |
Width of the errorbar arrow |
Plot (no return value)
# Example 1 plot(c(1, 2), c(450, 500), xlim = c(0.5, 2.5), ylim = c(400, 600), type = "o") addErrorBars(c(1, 2), c(450, 500), errorSize = c(20, 20)) # Example 2 plot(c(1, 2), c(450, 500), xlim = c(0.5, 2.5), ylim = c(400, 600), type = "o") addErrorBars(c(1, 2), c(450, 500), errorSize = c(20, 40), arrowSize = 0.1)# Example 1 plot(c(1, 2), c(450, 500), xlim = c(0.5, 2.5), ylim = c(400, 600), type = "o") addErrorBars(c(1, 2), c(450, 500), errorSize = c(20, 20)) # Example 2 plot(c(1, 2), c(450, 500), xlim = c(0.5, 2.5), ylim = c(400, 600), type = "o") addErrorBars(c(1, 2), c(450, 500), errorSize = c(20, 40), arrowSize = 0.1)
Calculate bin probabilities in observed data
calculateBinProbabilities(resOb, quantileType = 5)calculateBinProbabilities(resOb, quantileType = 5)
resOb |
Observed data (see dmcObservedData) |
quantileType |
Argument (1-9) from R function quantile specifying the algorithm (?quantile) |
resOb Observed data with additional $probSubject/$prob table
# Example 1: resOb <- flankerData resOb <- calculateBinProbabilities(resOb) resOb$prob# Example 1: resOb <- flankerData resOb <- calculateBinProbabilities(resOb) resOb$prob
Calculate conditional accuracy function (CAF). The DataFrame should contain columns defining the participant, compatibility condition, RT and error (Default column names: "Subject", "Comp", "RT", "Error"). The "Comp" column should define compatibility condition (Default: c("comp", "incomp")) and the "Error" column should define if the trial was an error or not (Default: c(0, 1) ).
calculateCAF( dat, nCAF = 5, columns = c("Subject", "Comp", "RT", "Error"), compCoding = c("comp", "incomp"), errorCoding = c(0, 1) )calculateCAF( dat, nCAF = 5, columns = c("Subject", "Comp", "RT", "Error"), compCoding = c("comp", "incomp"), errorCoding = c(0, 1) )
dat |
DataFrame with columns containing the participant number, condition compatibility, RT data (in ms) and an Error column. |
nCAF |
Number of CAF bins. |
columns |
Name of required columns Default: c("Subject", "Comp", "RT", "Error") |
compCoding |
Coding for compatibility Default: c("comp", "incomp") |
errorCoding |
Coding for errors Default: c(0, 1)) |
calculateCAF returns a DataFrame with conditional accuracy function (CAF) data (Bin, comp, incomp, effect)
# Example 1 dat <- createDF(nSubjects = 1, nTrl = 10000, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(500, 80, 100), "Comp_incomp" = c(600, 80, 140)), Error = list("Comp_comp" = c( 5, 4, 3, 2, 1), "Comp_incomp" = c(20, 8, 6, 4, 2))) caf <- calculateCAF(dat) # Example 2 dat <- createDF(nSubjects = 1, nTrl = 10000, design = list("Congruency" = c("cong", "incong"))) dat <- addDataDF(dat, RT = list("Congruency_cong" = c(500, 80, 100), "Congruency_incong" = c(600, 80, 140)), Error = list("Congruency_cong" = c( 5, 4, 3, 2, 1), "Congruency_incong" = c(20, 8, 6, 4, 2))) head(dat) caf <- calculateCAF(dat, columns = c("Subject", "Congruency", "RT", "Error"), compCoding = c("cong", "incong"))# Example 1 dat <- createDF(nSubjects = 1, nTrl = 10000, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(500, 80, 100), "Comp_incomp" = c(600, 80, 140)), Error = list("Comp_comp" = c( 5, 4, 3, 2, 1), "Comp_incomp" = c(20, 8, 6, 4, 2))) caf <- calculateCAF(dat) # Example 2 dat <- createDF(nSubjects = 1, nTrl = 10000, design = list("Congruency" = c("cong", "incong"))) dat <- addDataDF(dat, RT = list("Congruency_cong" = c(500, 80, 100), "Congruency_incong" = c(600, 80, 140)), Error = list("Congruency_cong" = c( 5, 4, 3, 2, 1), "Congruency_incong" = c(20, 8, 6, 4, 2))) head(dat) caf <- calculateCAF(dat, columns = c("Subject", "Congruency", "RT", "Error"), compCoding = c("cong", "incong"))
Calculate cost value (fit) using chi-square (CS) from correct and incorrect RT data.
calculateCostValueCS(resTh, resOb)calculateCostValueCS(resTh, resOb)
resTh |
list containing simulation $sim values (output from dmcSim) for rts_comp, rts_incomp, errs_comp, errs_incomp |
resOb |
list containing raw observed data (see dmcObservedData with keepRaw = TRUE) |
cost value (CS)
# Example 1: resTh <- dmcSim() resOb <- flankerData resOb <- calculateBinProbabilities(resOb) cost <- calculateCostValueCS(resTh, resOb)# Example 1: resTh <- dmcSim() resOb <- flankerData resOb <- calculateBinProbabilities(resOb) cost <- calculateCostValueCS(resTh, resOb)
Calculate cost value (fit) using likelihood-ratio chi-square statistic (GS) from correct and incorrect RT data.
calculateCostValueGS(resTh, resOb)calculateCostValueGS(resTh, resOb)
resTh |
list containing simulation $sim values (output from dmcSim) for rts_comp, rts_incomp, errs_comp, errs_incomp |
resOb |
list containing raw observed data (see dmcObservedData with keepRaw = TRUE) |
cost value (GS)
# Example 1: resTh <- dmcSim() resOb <- flankerData resOb <- calculateBinProbabilities(resOb) cost <- calculateCostValueGS(resTh, resOb)# Example 1: resTh <- dmcSim() resOb <- flankerData resOb <- calculateBinProbabilities(resOb) cost <- calculateCostValueGS(resTh, resOb)
Calculate cost value (fit) using root-mean-square error (RMSE) from a combination of RT and error rate.
calculateCostValueRMSE(resTh, resOb)calculateCostValueRMSE(resTh, resOb)
resTh |
list containing caf values for comp/incomp conditions (nbins * 4 columns) and delta values for comp/incomp conditions (nbins * 5 columns). See output from dmcSim (.$caf). |
resOb |
list containing caf values for comp/incomp conditions (n * 4 columns) and delta values for comp/incomp conditions (nbins * 5 columns). See output from dmcSim (.$delta). |
cost value (RMSE)
# Example 1: resTh <- dmcSim() resOb <- dmcSim() cost <- calculateCostValueRMSE(resTh, resOb) # Example 2: resTh <- dmcSim() resOb <- dmcSim(tau = 150) cost <- calculateCostValueRMSE(resTh, resOb)# Example 1: resTh <- dmcSim() resOb <- dmcSim() cost <- calculateCostValueRMSE(resTh, resOb) # Example 2: resTh <- dmcSim() resOb <- dmcSim(tau = 150) cost <- calculateCostValueRMSE(resTh, resOb)
Calculate cost value (fit) using squared percentage errror (SPE) from combination of RT and error rate.
calculateCostValueSPE(resTh, resOb)calculateCostValueSPE(resTh, resOb)
resTh |
list containing caf values for comp/incomp conditions (nbins * 4 columns) and delta values for comp/incomp conditions (nbins * 5 columns). See output from dmcSim (.$caf). |
resOb |
list containing caf values for comp/incomp conditions (n * 4 columns) and delta values for comp/incomp conditions (nbins * 5 columns). See output from dmcSim (.$delta). |
cost value (SPE)
# Example 1: resTh <- dmcSim() resOb <- dmcSim() cost <- calculateCostValueSPE(resTh, resOb) # Example 2: resTh <- dmcSim() resOb <- dmcSim(tau = 150) cost <- calculateCostValueSPE(resTh, resOb)# Example 1: resTh <- dmcSim() resOb <- dmcSim() cost <- calculateCostValueSPE(resTh, resOb) # Example 2: resTh <- dmcSim() resOb <- dmcSim(tau = 150) cost <- calculateCostValueSPE(resTh, resOb)
Calculate delta plot. Here RTs are split into n bins (Default: 5) for compatible and incompatible trials separately. Mean RT is calculated for each condition in each bin then subtracted (incompatible - compatible) to give a compatibility effect (delta) at each bin.
calculateDelta( dat, nDelta = 19, tDelta = 1, columns = c("Subject", "Comp", "RT"), compCoding = c("comp", "incomp"), quantileType = 5 )calculateDelta( dat, nDelta = 19, tDelta = 1, columns = c("Subject", "Comp", "RT"), compCoding = c("comp", "incomp"), quantileType = 5 )
dat |
DataFrame with columns containing the participant number, condition compatibility, and RT data (in ms). |
nDelta |
The number of delta bins. |
tDelta |
type of delta calculation (1=direct percentiles points, 2=percentile bounds (tile) averaging) |
columns |
Name of required columns Default: c("Subject", "Comp", "RT") |
compCoding |
Coding for compatibility Default: c("comp", "incomp") |
quantileType |
Argument (1-9) from R function quantile specifying the algorithm (?quantile) |
calculateDelta returns a DataFrame with distributional delta analysis data (Bin, comp, incomp, meanBin, Effect)
# Example 1 dat <- createDF(nSubjects = 1, nTrl = 10000, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(500, 80, 100), "Comp_incomp" = c(600, 80, 140))) delta <- calculateDelta(dat) # Example 2 dat <- createDF(nSubject = 1, nTrl = 10000, design = list("Congruency" = c("cong", "incong"))) dat <- addDataDF(dat, RT = list("Congruency_cong" = c(500, 80, 100), "Congruency_incong" = c(600, 80, 140))) head(dat) delta <- calculateDelta(dat, nDelta = 9, columns = c("Subject", "Congruency", "RT"), compCoding = c("cong", "incong"))# Example 1 dat <- createDF(nSubjects = 1, nTrl = 10000, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(500, 80, 100), "Comp_incomp" = c(600, 80, 140))) delta <- calculateDelta(dat) # Example 2 dat <- createDF(nSubject = 1, nTrl = 10000, design = list("Congruency" = c("cong", "incong"))) dat <- addDataDF(dat, RT = list("Congruency_cong" = c(500, 80, 100), "Congruency_incong" = c(600, 80, 140))) head(dat) delta <- calculateDelta(dat, nDelta = 9, columns = c("Subject", "Congruency", "RT"), compCoding = c("cong", "incong"))
Create dataframe (see also addDataDF)
createDF( nSubjects = 20, nTrl = 50, design = list(A = c("A1", "A2"), B = c("B1", "B2")) )createDF( nSubjects = 20, nTrl = 50, design = list(A = c("A1", "A2"), B = c("B1", "B2")) )
nSubjects |
Number of subjects |
nTrl |
Number of trials per factor/level for each participant |
design |
Factors and levels |
DataFrame with Subject, Factor(s) columns
# Example 1 dat <- createDF() # Example 2 dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp"))) # Example 3 dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp"), "Side" = c("left", "right", "middle")))# Example 1 dat <- createDF() # Example 2 dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp"))) # Example 3 dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp"), "Side" = c("left", "right", "middle")))
Combine observed datasets
dmcCombineObservedData(...)dmcCombineObservedData(...)
... |
Any number of outputs from dmcObservedData |
dmcCombineObservedData returns a list of objects of class "dmcob"
# Example 1 dat <- dmcCombineObservedData(flankerData, simonData) # combine flanker/simon data plot(dat, figType = "delta", xlimDelta = c(200, 700), ylimDelta = c(-20, 80), cols = c("black", "darkgrey"), legend.parameters = list(x=200, y=80, legend = c("Flanker Task", "Simon Task")))# Example 1 dat <- dmcCombineObservedData(flankerData, simonData) # combine flanker/simon data plot(dat, figType = "delta", xlimDelta = c(200, 700), ylimDelta = c(-20, 80), cols = c("black", "darkgrey"), legend.parameters = list(x=200, y=80, legend = c("Flanker Task", "Simon Task")))
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").
dmcFit( 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, spDist = 1, drOnset = 0, drDist = 0, drShape = 3, drLim = c(0.1, 0.7), rtMax = 5000, costFunction = "RMSE", printInputArgs = TRUE, printResults = FALSE, optimControl = list(), numCores = 2 )dmcFit( 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, spDist = 1, drOnset = 0, drDist = 0, drShape = 3, drLim = c(0.1, 0.7), rtMax = 5000, costFunction = "RMSE", 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 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 |
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 |
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 |
rtMax |
The limit on simulated RT (decision + non-decisional components) |
costFunction |
The cost function to minimise: root mean square error ("RMSE": default), squared percentage error ("SPE"), or likelihood-ratio chi-square statistic ("GS") |
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 |
dmcfit returns an object of class "dmcfit" with the following components:
sim |
Individual trial data points (RTs for all trial types e.g., correct/error trials) and activation vectors from the simulation |
summary |
Condition means for reaction time and error rate |
caf |
Conditional Accuracy Function (CAF) data per bin |
delta |
DataFrame with distributional delta analysis data correct trials (Bin, meanComp, meanIncomp, meanBin, meanEffect) |
delta_errs |
DataFrame with distributional delta analysis data incorrect trials (Bin, meanComp, meanIncomp, meanBin, meanEffect) |
par |
The fitted model parameters + final cost value of the fit |
# Code below can exceed CRAN check time limit, hence donttest # Example 1: Flanker data from Ulrich et al. (2015) fit <- dmcFit(flankerData) # only initial search tau plot(fit, flankerData) summary(fit) # Example 2: Simon data from Ulrich et al. (2015) fit <- dmcFit(simonData) # only initial search tau plot(fit, simonData) summary(fit) # Example 3: Flanker data from Ulrich et al. (2015) with non-default # start vals and some fixed values fit <- dmcFit(flankerData, startVals = list(drc = 0.6, aaShape = 2.5), fixedFit = list(drc = TRUE, aaShape = TRUE) ) # Example 4: Simulated Data (+ve going delta function) dat <- createDF(nSubjects = 20, nTrl = 500, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list( "Comp_comp" = c(510, 100, 100), "Comp_incomp" = c(540, 130, 85) ), Error = list( "Comp_comp" = c(4, 3, 2, 1, 1), "Comp_incomp" = c(20, 4, 3, 1, 1) ) ) datOb <- dmcObservedData(dat, columns = c("Subject", "Comp", "RT", "Error")) plot(datOb) fit <- dmcFit(datOb, nTrl = 5000) plot(fit, datOb) summary(fit) # Example 5: Fitting 2+ datasets within all common parameters values fit <- dmcFit(list(flankerData, simonData), nTrl=1000) plot(fit[[1]], flankerData) plot(fit[[2]], simonData) summary(fit) # Example 6: Fitting 2+ datasets within some parameters values varying fit <- dmcFit(list(flankerData, simonData), freeCombined=list(amp=TRUE, tau=TRUE), nTrl=1000) summary(fit) # NB. amp/tau values different, other parameter values equal# Code below can exceed CRAN check time limit, hence donttest # Example 1: Flanker data from Ulrich et al. (2015) fit <- dmcFit(flankerData) # only initial search tau plot(fit, flankerData) summary(fit) # Example 2: Simon data from Ulrich et al. (2015) fit <- dmcFit(simonData) # only initial search tau plot(fit, simonData) summary(fit) # Example 3: Flanker data from Ulrich et al. (2015) with non-default # start vals and some fixed values fit <- dmcFit(flankerData, startVals = list(drc = 0.6, aaShape = 2.5), fixedFit = list(drc = TRUE, aaShape = TRUE) ) # Example 4: Simulated Data (+ve going delta function) dat <- createDF(nSubjects = 20, nTrl = 500, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list( "Comp_comp" = c(510, 100, 100), "Comp_incomp" = c(540, 130, 85) ), Error = list( "Comp_comp" = c(4, 3, 2, 1, 1), "Comp_incomp" = c(20, 4, 3, 1, 1) ) ) datOb <- dmcObservedData(dat, columns = c("Subject", "Comp", "RT", "Error")) plot(datOb) fit <- dmcFit(datOb, nTrl = 5000) plot(fit, datOb) summary(fit) # Example 5: Fitting 2+ datasets within all common parameters values fit <- dmcFit(list(flankerData, simonData), nTrl=1000) plot(fit[[1]], flankerData) plot(fit[[2]], simonData) summary(fit) # Example 6: Fitting 2+ datasets within some parameters values varying fit <- dmcFit(list(flankerData, simonData), freeCombined=list(amp=TRUE, tau=TRUE), nTrl=1000) summary(fit) # NB. amp/tau values different, other parameter values equal
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").
dmcFitDE( resOb, nTrl = 1e+05, minVals = list(), maxVals = list(), fixedFit = list(), freeCombined = list(), nCAF = 5, nDelta = 19, pDelta = vector(), tDelta = 1, deltaErrors = FALSE, spDist = 1, drOnset = 0, drDist = 0, drShape = 3, drLim = c(0.1, 0.7), rtMax = 5000, costFunction = "RMSE", deControl = list(), numCores = 2 )dmcFitDE( resOb, nTrl = 1e+05, minVals = list(), maxVals = list(), fixedFit = list(), freeCombined = list(), nCAF = 5, nDelta = 19, pDelta = vector(), tDelta = 1, deltaErrors = FALSE, spDist = 1, drOnset = 0, drDist = 0, drShape = 3, drLim = c(0.1, 0.7), rtMax = 5000, costFunction = "RMSE", 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, 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, 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)) |
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 |
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" information) (> 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) |
costFunction |
The cost function to minimise: root mean square error ("RMSE": default), squared percentage error ("SPE"), or likelihood-ratio chi-square statistic ("GS") |
deControl |
Additional control parameters passed to DEoptim (see DEoptim.control) |
numCores |
Number of cores to use |
dmcfit returns an object of class "dmcfit" with the following components:
sim |
Individual trial data points (RTs for all trial types e.g., correct/error trials) and activation vectors from the simulation |
summary |
Condition means for reaction time and error rate |
caf |
Conditional Accuracy Function (CAF) data per bin |
delta |
DataFrame with distributional delta analysis data correct trials (Bin, meanComp, meanIncomp, meanBin, meanEffect) |
delta_errs |
Optional: DataFrame with distributional delta analysis data incorrect trials (Bin, meanComp, meanIncomp, meanBin, meanEffect) |
par |
The fitted model parameters + final cost value of the fit |
# The code below can exceed CRAN check time limit, hence donttest # NB. The following code when using numCores = 2 (default) takes approx 20 minutes on # a standard desktop, whilst when increasing the number of cores used, (numCores = 12), # the code takes approx 5 minutes. # Example 1: Flanker data from Ulrich et al. (2015) fit <- dmcFitDE(flankerData, nTrl = 1000); plot(fit, flankerData) summary(fit) # Example 2: Simon data from Ulrich et al. (2015) fit <- dmcFitDE(simonData, nTrl = 5000, deControl = list(itermax=30)) plot(fit, simonData) summary(fit)# The code below can exceed CRAN check time limit, hence donttest # NB. The following code when using numCores = 2 (default) takes approx 20 minutes on # a standard desktop, whilst when increasing the number of cores used, (numCores = 12), # the code takes approx 5 minutes. # Example 1: Flanker data from Ulrich et al. (2015) fit <- dmcFitDE(flankerData, nTrl = 1000); plot(fit, flankerData) summary(fit) # Example 2: Simon data from Ulrich et al. (2015) fit <- dmcFitDE(simonData, nTrl = 5000, deControl = list(itermax=30)) plot(fit, simonData) summary(fit)
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(), freeCombined = list(), nCAF = 5, nDelta = 19, pDelta = vector(), tDelta = 1, deltaErrors = FALSE, spDist = 1, drOnset = 0, drDist = 0, drShape = 3, drLim = c(0.1, 0.7), rtMax = 5000, costFunction = "RMSE", subjects = c(), printInputArgs = TRUE, printResults = FALSE, optimControl = list(), numCores = 2 )dmcFitSubject( resOb, nTrl = 1e+05, startVals = list(), minVals = list(), maxVals = list(), fixedFit = list(), fitInitialGrid = TRUE, fitInitialGridN = 10, fixedGrid = list(), freeCombined = list(), nCAF = 5, nDelta = 19, pDelta = vector(), tDelta = 1, deltaErrors = FALSE, spDist = 1, drOnset = 0, drDist = 0, drShape = 3, drLim = c(0.1, 0.7), rtMax = 5000, costFunction = "RMSE", 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 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. |
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. |
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 |
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 |
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 |
rtMax |
The limit on simulated RT (decision + non-decisional components) |
costFunction |
The cost function to minimise: root mean square error ("RMSE": default), squared percentage error ("SPE"), or likelihood-ratio chi-square statistic ("GS") |
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)# 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)
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 )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)# 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)
Basic analysis to create data object required for observed data. Example raw *.txt files are flankerData.txt and simonData.txt. There are four critical columns:
column containing subject number
column coding for compatible or incompatible
column with RT (in ms)
column indicating of the response was correct
dmcObservedData( dat, nCAF = 5, nDelta = 19, pDelta = vector(), tDelta = 1, outlier = c(200, 1200), columns = c("Subject", "Comp", "RT", "Error"), compCoding = c("comp", "incomp"), errorCoding = c(0, 1), quantileType = 5, deltaErrors = FALSE, keepRaw = FALSE, delim = "\t", skip = 0 )dmcObservedData( dat, nCAF = 5, nDelta = 19, pDelta = vector(), tDelta = 1, outlier = c(200, 1200), columns = c("Subject", "Comp", "RT", "Error"), compCoding = c("comp", "incomp"), errorCoding = c(0, 1), quantileType = 5, deltaErrors = FALSE, keepRaw = FALSE, delim = "\t", skip = 0 )
dat |
A text file(s) containing the observed data or an R DataFrame (see createDF/addDataDF) |
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) |
outlier |
Outlier limits in ms (e.g., c(200, 1200)) |
columns |
Name of required columns DEFAULT = c("Subject", "Comp", "RT", "Error") |
compCoding |
Coding for compatibility DEFAULT = c("comp", "incomp") |
errorCoding |
Coding for errors DEFAULT = c(0, 1)) |
quantileType |
Argument (1-9) from R function quantile specifying the algorithm (?quantile) |
deltaErrors |
TRUE/FALSE Calculate RT delta for error trials. |
keepRaw |
TRUE/FALSE |
delim |
Single character used to separate fields within a record if reading from external text file. |
skip |
The number of lines to skip before reading data if reading from external text file. |
dmcObservedData returns an object of class "dmcob" with the following components:
summarySubject |
DataFrame within individual subject data (rtCor, perErr, rtErr) for compatibility condition |
summary |
DataFrame within aggregated subject data (rtCor, sdRtCor, seRtCor, perErr, sdPerErr, sePerErr, rtErr, sdRtErr, seRtErr) for compatibility condition |
cafSubject |
DataFrame within individual subject conditional accuracy function (CAF) data (Bin, accPerComp, accPerIncomp, meanEffect) |
caf |
DataFrame within aggregated subject conditional accuracy function (CAF) data (Bin, accPerComp, accPerIncomp, meanEffect, sdEffect, seEffect) |
deltaSubject |
DataFrame within individual subject distributional delta analysis data correct trials (Bin, meanComp, meanIncomp, meanBin, meanEffect) |
delta |
DataFrame within aggregated subject distributional delta analysis data correct trials (Bin, meanComp, meanIncomp, meanBin, meanEffect, sdEffect, seEffect) |
deltaErrorsSubject |
Optional: DataFrame within individual subject distributional delta analysis data incorrect trials (Bin, meanComp, meanIncomp, meanBin, meanEffect) |
deltaErrors |
Optional: DataFrame within aggregated subject distributional delta analysis data incorrect trials (Bin, meanComp, meanIncomp, meanBin, meanEffect, sdEffect, seEffect) |
# Example 1 plot(flankerData) # flanker data from Ulrich et al. (2015) plot(simonData) # simon data from Ulrich et al. (2015) # Example 2 (Basic behavioural analysis from Ulrich et al. ) flankerDat <- cbind(Task = "flanker", flankerData$summarySubject) simonDat <- cbind(Task = "simon", simonData$summarySubject) datAgg <- rbind(flankerDat, simonDat) datAgg$Subject <- factor(datAgg$Subject) datAgg$Task <- factor(datAgg$Task) datAgg$Comp <- factor(datAgg$Comp) aovErr <- aov(perErr ~ Comp*Task + Error(Subject/(Comp*Task)), datAgg) summary(aovErr) model.tables(aovErr, type = "mean") aovRt <- aov(rtCor ~ Comp*Task + Error(Subject/(Comp*Task)), datAgg) summary(aovRt) model.tables(aovRt, type = "mean") # Example 3 dat <- createDF(nSubjects = 50, nTrl = 500, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(500, 75, 120), "Comp_incomp" = c(530, 75, 100)), Error = list("Comp_comp" = c(3, 2, 2, 1, 1), "Comp_incomp" = c(21, 3, 2, 1, 1))) datOb <- dmcObservedData(dat) plot(datOb) plot(datOb, subject = 1) # Example 4 dat <- createDF(nSubjects = 50, nTrl = 500, design = list("Congruency" = c("cong", "incong"))) dat <- addDataDF(dat, RT = list("Congruency_cong" = c(500, 75, 100), "Congruency_incong" = c(530, 100, 110)), Error = list("Congruency_cong" = c(3, 2, 2, 1, 1), "Congruency_incong" = c(21, 3, 2, 1, 1))) datOb <- dmcObservedData(dat, nCAF = 5, nDelta = 9, columns = c("Subject", "Congruency", "RT", "Error"), compCoding = c("cong", "incong")) plot(datOb, labels = c("Congruent", "Incongruent")) plot(datOb, subject = 1)# Example 1 plot(flankerData) # flanker data from Ulrich et al. (2015) plot(simonData) # simon data from Ulrich et al. (2015) # Example 2 (Basic behavioural analysis from Ulrich et al. ) flankerDat <- cbind(Task = "flanker", flankerData$summarySubject) simonDat <- cbind(Task = "simon", simonData$summarySubject) datAgg <- rbind(flankerDat, simonDat) datAgg$Subject <- factor(datAgg$Subject) datAgg$Task <- factor(datAgg$Task) datAgg$Comp <- factor(datAgg$Comp) aovErr <- aov(perErr ~ Comp*Task + Error(Subject/(Comp*Task)), datAgg) summary(aovErr) model.tables(aovErr, type = "mean") aovRt <- aov(rtCor ~ Comp*Task + Error(Subject/(Comp*Task)), datAgg) summary(aovRt) model.tables(aovRt, type = "mean") # Example 3 dat <- createDF(nSubjects = 50, nTrl = 500, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(500, 75, 120), "Comp_incomp" = c(530, 75, 100)), Error = list("Comp_comp" = c(3, 2, 2, 1, 1), "Comp_incomp" = c(21, 3, 2, 1, 1))) datOb <- dmcObservedData(dat) plot(datOb) plot(datOb, subject = 1) # Example 4 dat <- createDF(nSubjects = 50, nTrl = 500, design = list("Congruency" = c("cong", "incong"))) dat <- addDataDF(dat, RT = list("Congruency_cong" = c(500, 75, 100), "Congruency_incong" = c(530, 100, 110)), Error = list("Congruency_cong" = c(3, 2, 2, 1, 1), "Congruency_incong" = c(21, 3, 2, 1, 1))) datOb <- dmcObservedData(dat, nCAF = 5, nDelta = 9, columns = c("Subject", "Congruency", "RT", "Error"), compCoding = c("cong", "incong")) plot(datOb, labels = c("Congruent", "Incongruent")) plot(datOb, subject = 1)
DMC model simulation detailed in Ulrich, R., Schroeter, H., Leuthold, H., & Birngruber, T. (2015). Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions. Cognitive Psychology, 78, 148-174. This function is essentially a wrapper around the c++ function runDMC
dmcSim( amp = 20, tau = 30, drc = 0.5, bnds = 75, resDist = 1, resMean = 300, resSD = 30, aaShape = 2, spShape = 3, sigm = 4, nTrl = 1e+05, tmax = 1000, spDist = 0, spLim = c(-75, 75), spBias = 0, drOnset = 0, drDist = 0, drShape = 3, drLim = c(0.1, 0.7), rtMax = 5000, fullData = FALSE, nTrlData = 5, nDelta = 9, pDelta = vector(), tDelta = 1, deltaErrors = FALSE, nCAF = 5, bndsRate = 0, bndsSaturation = 0, printInputArgs = TRUE, printResults = TRUE, setSeed = FALSE, seedValue = 1 )dmcSim( amp = 20, tau = 30, drc = 0.5, bnds = 75, resDist = 1, resMean = 300, resSD = 30, aaShape = 2, spShape = 3, sigm = 4, nTrl = 1e+05, tmax = 1000, spDist = 0, spLim = c(-75, 75), spBias = 0, drOnset = 0, drDist = 0, drShape = 3, drLim = c(0.1, 0.7), rtMax = 5000, fullData = FALSE, nTrlData = 5, nDelta = 9, pDelta = vector(), tDelta = 1, deltaErrors = FALSE, nCAF = 5, bndsRate = 0, bndsSaturation = 0, printInputArgs = TRUE, printResults = TRUE, setSeed = FALSE, seedValue = 1 )
amp |
amplitude of automatic activation |
tau |
time to peak automatic activation |
drc |
drift rate of controlled processes |
bnds |
+- response criterion |
resDist |
residual distribution type (1=normal, 2=uniform) |
resMean |
residual distribution mean |
resSD |
residual distribution standard deviation |
aaShape |
shape parameter of automatic activation |
spShape |
starting point (sp) shape parameter |
sigm |
diffusion constant |
nTrl |
number of trials |
tmax |
number of time points per trial |
spDist |
starting point (sp) distribution (0 = constant, 1 = beta, 2 = uniform) |
spLim |
starting point (sp) range |
spBias |
starting point (sp) bias |
drOnset |
drift rate (dr) onset (default=0; must be >= 0) |
drDist |
drift rate (dr) distribution type (0 = constant, 1 = beta, 2 = uniform) |
drShape |
drift rate (dr) shape parameter |
drLim |
drift rate (dr) range |
rtMax |
limit on simulated RT (decision + non-decisional component) |
fullData |
TRUE/FALSE (Default: FALSE) NB. only required when plotting activation function and/or individual trials |
nTrlData |
Number of trials to plot |
nDelta |
number of delta bins |
pDelta |
alternative to nDelta (tDelta = 1 only) by directly specifying required percentile values (0-100) |
tDelta |
type of delta calculation (1=direct percentiles points, 2=percentile bounds (tile) averaging) |
deltaErrors |
TRUE/FALSE Calculate delta bins for error trials |
nCAF |
Number of CAF bins |
bndsRate |
0 (default) = fixed bnds |
bndsSaturation |
bndsSaturatoin |
printInputArgs |
TRUE/FALSE |
printResults |
TRUE/FALSE |
setSeed |
TRUE/FALSE If true, set seed to seed value |
seedValue |
1 |
dmcSim returns an object of class "dmcsim" with the following components:
sim |
Individual trial data points (reaction times/error) and activation vectors from simulation |
summary |
Condition means for reaction time and error rate |
caf |
Accuracy per bin for compatible and incompatible trials |
delta |
Mean RT and compatibility effect per bin |
deltaErrors |
Optional output: Mean RT and compatibility effect per bin for error trials |
prms |
The input parameters used in the simulation |
# Example 1 dmc <- dmcSim(fullData = TRUE) # fullData only needed for activation/trials (left column plot) plot(dmc) dmc <- dmcSim() # faster! plot(dmc) # Example 2 dmc <- dmcSim(tau = 130) plot(dmc) # Example 3 dmc <- dmcSim(tau = 90) plot(dmc) # Example 4 dmc <- dmcSim(spDist = 1) plot(dmc, "delta") # Example 5 dmc <- dmcSim(tau = 130, drDist = 1) plot(dmc, "caf") # Example 6 dmc <- dmcSim(nDelta = 10, nCAF = 10) plot(dmc)# Example 1 dmc <- dmcSim(fullData = TRUE) # fullData only needed for activation/trials (left column plot) plot(dmc) dmc <- dmcSim() # faster! plot(dmc) # Example 2 dmc <- dmcSim(tau = 130) plot(dmc) # Example 3 dmc <- dmcSim(tau = 90) plot(dmc) # Example 4 dmc <- dmcSim(spDist = 1) plot(dmc, "delta") # Example 5 dmc <- dmcSim(tau = 130, drDist = 1) plot(dmc, "caf") # Example 6 dmc <- dmcSim(nDelta = 10, nCAF = 10) plot(dmc)
A shiny app allowing interactive exploration of DMC parameters
dmcSimApp()dmcSimApp()
Shiny App
Run dmcSim with range of input parameters.
dmcSims(params, printInputArgs = FALSE, printResults = FALSE)dmcSims(params, printInputArgs = FALSE, printResults = FALSE)
params |
(list of parameters to dmcSim) |
printInputArgs |
Print DMC input arguments to console |
printResults |
Print DMC output to console |
dmcSims returns a list of objects of class "dmcsim"
# Example 1 params <- list(amp = seq(10, 20, 5), tau = c(50, 100, 150), nTrl = 50000) dmc <- dmcSims(params) plot(dmc[[1]]) # full combination 1 plot(dmc) # delta plots for all combinations plot(dmc[c(1:3)]) # delta plots for specific combinations plot(dmc[c(1, 3)]) # delta plots for specific combinations # Example 2 params <- list(amp = seq(10, 20, 5), tau = seq(20, 40, 20), bnds = seq(50, 100, 25)) dmc <- dmcSims(params) plot(dmc[[1]]) # combination 1 plot(dmc, ncol = 2) # delta plots for all combinations plot(dmc[c(1:3)]) # delta plots for specific combinations# Example 1 params <- list(amp = seq(10, 20, 5), tau = c(50, 100, 150), nTrl = 50000) dmc <- dmcSims(params) plot(dmc[[1]]) # full combination 1 plot(dmc) # delta plots for all combinations plot(dmc[c(1:3)]) # delta plots for specific combinations plot(dmc[c(1, 3)]) # delta plots for specific combinations # Example 2 params <- list(amp = seq(10, 20, 5), tau = seq(20, 40, 20), bnds = seq(50, 100, 25)) dmc <- dmcSims(params) plot(dmc[[1]]) # combination 1 plot(dmc, ncol = 2) # delta plots for all combinations plot(dmc[c(1:3)]) # delta plots for specific combinations
Returns a random vector of 0's (correct) and 1's (incorrect) with defined proportions (default = 10% errors).
errDist(n = 10000, proportion = 10)errDist(n = 10000, proportion = 10)
n |
Number |
proportion |
Approximate proportion of errors in percentage |
double
# Example 1 x <- errDist(1000, 10) table(x)# Example 1 x <- errDist(1000, 10) table(x)
$summary –> Reaction time correct, standard deviation correct, standard error correct, percentage error, standard deviation error, standard error error, reaction time incorrect, standard deviation incorrect, and standard error incorrect trials for both compatible and incompatible trials
$caf –> Proportion correct for compatible and incompatible trials across 5 bins
$delta –> Compatible reactions times, incompatible mean reaction times, mean reaction times, incompatible - compatible reaction times (effect), and standard deviation + standard error of this effect across 19 bins
$data –> Raw data from flankerData.txt + additional outlier column
flankerDataflankerData
dmcob
Aggregate simulation results from dmcFitSubject/dmcFitSubjectDE.
## S3 method for class 'dmcfit_subject' mean(x, ...)## S3 method for class 'dmcfit_subject' mean(x, ...)
x |
Output from dmcFitSubject/dmcFitSubjectDE |
... |
pars |
mean.dmcfit return an object of class "dmcfit" with the following components:
summary |
DataFrame within aggregated subject data (rtCor, sdRtCor, seRtCor, perErr, sdPerErr, sePerErr, rtErr, sdRtErr, seRtErr) for compatibility condition |
delta |
DataFrame within aggregated subject distributional delta analysis data correct trials (Bin, meanComp, meanIncomp, meanBin, meanEffect, sdEffect, seEffect) |
caf |
DataFrame within aggregated subject conditional accuracy function (CAF) data (Bin, accPerComp, accPerIncomp, meanEffect, sdEffect, seEffect) |
par |
The fitted model parameters + final cost value of the fit |
# Code below can exceed CRAN check time limit, hence donttest # Example 1: Fit individual data then aggregate fitSubjects <- dmcFitSubject(flankerData, nTrl = 1000, subjects = c(1, 2)) fitAgg <- mean(fitSubjects) plot(fitAgg, flankerData)# Code below can exceed CRAN check time limit, hence donttest # Example 1: Fit individual data then aggregate fitSubjects <- dmcFitSubject(flankerData, nTrl = 1000, subjects = c(1, 2)) fitAgg <- mean(fitSubjects) plot(fitAgg, flankerData)
Plot the simulation results from the output of dmcFit. The plot can be an overall summary, or individual plots (activation, trials, pdf, cdf, caf, delta, all). Plot type summary1 contains an activation plot, example individual trials, the probability distribution function (PDF), the cumulative distribution function (CDF), the conditional accuracy function (CAF) and delta plots. This required that dmcSim is run with fullData = TRUE. Plot type summary2 contains only the PDF, CDF, CAF and delta plots and does not require that dmcSim is run with fullData = TRUE.
## S3 method for class 'dmcfit' plot( x, y, figType = "summary", labels = c("Compatible", "Incompatible", "Observed", "Predicted"), cols = c("green", "red"), ylimRt = NULL, ylimErr = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, legend.parameters = list(legend = c("Observed", "Predicted")), ... )## S3 method for class 'dmcfit' plot( x, y, figType = "summary", labels = c("Compatible", "Incompatible", "Observed", "Predicted"), cols = c("green", "red"), ylimRt = NULL, ylimErr = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, legend.parameters = list(legend = c("Observed", "Predicted")), ... )
x |
Output from dmcFit |
y |
Observed data |
figType |
summary, rtCorrect, errorRate, rtErrors, cdf, caf, delta, all |
labels |
Condition labels c("Compatible", "Incompatible", "Observed", "Predicted") default |
cols |
Condition colours c("green", "red") default |
ylimRt |
ylimit for Rt plots |
ylimErr |
ylimit for error rate plots |
xlimCDF |
ylimit for CDF plot |
ylimCAF |
ylimit for CAF plot |
cafBinLabels |
TRUE/FALSE |
ylimDelta |
ylimit for delta plot |
xlimDelta |
xlimit for delta plot |
xlabs |
TRUE/FALSE |
ylabs |
TRUE/FALSE |
xaxts |
TRUE/FALSE |
yaxts |
TRUE/FALSE |
xylabPos |
2 |
resetPar |
TRUE/FALSE Reset graphical parameters |
legend |
TRUE/FALSE |
legend.parameters |
list |
... |
additional plot pars |
Plot (no return value)
# Example 1 resTh <- dmcFit(flankerData, nTrl = 5000) plot(resTh, flankerData) plot(resTh, flankerData, figType = "deltaErrors") # Example 2 resTh <- dmcFit(simonData, nTrl = 5000) plot(resTh, simonData)# Example 1 resTh <- dmcFit(flankerData, nTrl = 5000) plot(resTh, flankerData) plot(resTh, flankerData, figType = "deltaErrors") # Example 2 resTh <- dmcFit(simonData, nTrl = 5000) plot(resTh, simonData)
Plot the simulation results from the output of dmcFit. The plot can be an overall summary, or individual plots (activation, trials, pdf, cdf, caf, delta, all). Plot type summary1 contains an activation plot, example individual trials, the probability distribution function (PDF), the cumulative distribution function (CDF), the conditional accuracy function (CAF) and delta plots. This required that dmcSim is run with fullData = TRUE. Plot type summary2 contains only the PDF, CDF, CAF and delta plots and does not require that dmcSim is run with fullData = TRUE.
## S3 method for class 'dmcfit_subject' plot( x, y, subject = NULL, figType = "summary", labels = c("Compatible", "Incompatible", "Observed", "Predicted"), cols = c("green", "red"), ylimRt = NULL, ylimErr = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, legend.parameters = list(legend = c("Observed", "Predicted")), ... )## S3 method for class 'dmcfit_subject' plot( x, y, subject = NULL, figType = "summary", labels = c("Compatible", "Incompatible", "Observed", "Predicted"), cols = c("green", "red"), ylimRt = NULL, ylimErr = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, legend.parameters = list(legend = c("Observed", "Predicted")), ... )
x |
Output from dmcFit |
y |
Observed data |
subject |
NULL (aggregated data across all subjects) or integer for subject number |
figType |
summary, rtCorrect, errorRate, rtErrors, cdf, caf, delta, all |
labels |
Condition labels c("Compatible", "Incompatible", "Observed", "Predicted") default |
cols |
Condition colours c("green", "red") default |
ylimRt |
ylimit for Rt plots |
ylimErr |
ylimit for error rate plots |
xlimCDF |
ylimit for CDF plot |
ylimCAF |
ylimit for CAF plot |
cafBinLabels |
TRUE/FALSE |
ylimDelta |
ylimit for delta plot |
xlimDelta |
xlimit for delta plot |
xlabs |
TRUE/FALSE |
ylabs |
TRUE/FALSE |
xaxts |
TRUE/FALSE |
yaxts |
TRUE/FALSE |
xylabPos |
2 |
resetPar |
TRUE/FALSE Reset graphical parameters |
legend |
TRUE/FALSE |
legend.parameters |
list |
... |
additional plot pars |
Plot (no return value)
# Example 1 resTh <- dmcFitSubject(flankerData, nTrl = 5000, subject = c(1,3)) plot(resTh, flankerData, subject = 3)# Example 1 resTh <- dmcFitSubject(flankerData, nTrl = 5000, subject = c(1,3)) plot(resTh, flankerData, subject = 3)
Plot the simulation results from the output of dmcFit. The plot can be an overall summary, or individual plots (activation, trials, pdf, cdf, caf, delta, all). Plot type summary1 contains an activation plot, example individual trials, the probability distribution function (PDF), the cumulative distribution function (CDF), the conditional accuracy function (CAF) and delta plots. This required that dmcSim is run with fullData = TRUE. Plot type summary2 contains only the PDF, CDF, CAF and delta plots and does not require that dmcSim is run with fullData = TRUE.
## S3 method for class 'dmcfits' plot( x, y, figType = "summary", labels = c("Compatible", "Incompatible", "Observed", "Predicted"), cols = c("green", "red"), ylimRt = NULL, ylimErr = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, legend.parameters = list(legend = c("Observed", "Predicted")), ... )## S3 method for class 'dmcfits' plot( x, y, figType = "summary", labels = c("Compatible", "Incompatible", "Observed", "Predicted"), cols = c("green", "red"), ylimRt = NULL, ylimErr = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, legend.parameters = list(legend = c("Observed", "Predicted")), ... )
x |
Output from dmcFit |
y |
Observed data |
figType |
summary, rtCorrect, errorRate, rtErrors, cdf, caf, delta, all |
labels |
Condition labels c("Compatible", "Incompatible", "Observed", "Predicted") default |
cols |
Condition colours c("green", "red") default |
ylimRt |
ylimit for Rt plots |
ylimErr |
ylimit for error rate plots |
xlimCDF |
ylimit for CDF plot |
ylimCAF |
ylimit for CAF plot |
cafBinLabels |
TRUE/FALSE |
ylimDelta |
ylimit for delta plot |
xlimDelta |
xlimit for delta plot |
xlabs |
TRUE/FALSE |
ylabs |
TRUE/FALSE |
xaxts |
TRUE/FALSE |
yaxts |
TRUE/FALSE |
xylabPos |
2 |
resetPar |
TRUE/FALSE Reset graphical parameters |
legend |
TRUE/FALSE |
legend.parameters |
list |
... |
additional plot pars |
Plot (no return value)
# Example 1 resTh <- dmcFit(flankerData, nTrl = 5000) plot(resTh, flankerData) plot(resTh, flankerData, figType = "deltaErrors") # Example 2 resTh <- dmcFit(simonData, nTrl = 5000) plot(resTh, simonData)# Example 1 resTh <- dmcFit(flankerData, nTrl = 5000) plot(resTh, flankerData) plot(resTh, flankerData, figType = "deltaErrors") # Example 2 resTh <- dmcFit(simonData, nTrl = 5000) plot(resTh, simonData)
Plot the simulation results from the output of dmcFit. The plot can be an overall summary, or individual plots (activation, trials, pdf, cdf, caf, delta, all). Plot type summary1 contains an activation plot, example individual trials, the probability distribution function (PDF), the cumulative distribution function (CDF), the conditional accuracy function (CAF) and delta plots. This required that dmcSim is run with fullData = TRUE. Plot type summary2 contains only the PDF, CDF, CAF and delta plots and does not require that dmcSim is run with fullData = TRUE.
## S3 method for class 'dmcfits_subject' plot( x, y, subject = NULL, figType = "summary", labels = c("Compatible", "Incompatible", "Observed", "Predicted"), cols = c("green", "red"), ylimRt = NULL, ylimErr = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, legend.parameters = list(legend = c("Observed", "Predicted")), ... )## S3 method for class 'dmcfits_subject' plot( x, y, subject = NULL, figType = "summary", labels = c("Compatible", "Incompatible", "Observed", "Predicted"), cols = c("green", "red"), ylimRt = NULL, ylimErr = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, legend.parameters = list(legend = c("Observed", "Predicted")), ... )
x |
Output from dmcFit |
y |
Observed data |
subject |
NULL (aggregated data across all subjects) or integer for subject number |
figType |
summary, rtCorrect, errorRate, rtErrors, cdf, caf, delta, all |
labels |
Condition labels c("Compatible", "Incompatible", "Observed", "Predicted") default |
cols |
Condition colours c("green", "red") default |
ylimRt |
ylimit for Rt plots |
ylimErr |
ylimit for error rate plots |
xlimCDF |
ylimit for CDF plot |
ylimCAF |
ylimit for CAF plot |
cafBinLabels |
TRUE/FALSE |
ylimDelta |
ylimit for delta plot |
xlimDelta |
xlimit for delta plot |
xlabs |
TRUE/FALSE |
ylabs |
TRUE/FALSE |
xaxts |
TRUE/FALSE |
yaxts |
TRUE/FALSE |
xylabPos |
2 |
resetPar |
TRUE/FALSE Reset graphical parameters |
legend |
TRUE/FALSE |
legend.parameters |
list |
... |
additional plot pars |
Plot (no return value)
# Example 1 resTh <- dmcFit(flankerData, nTrl = 5000) plot(resTh, flankerData) plot(resTh, flankerData, figType = "deltaErrors") # Example 2 resTh <- dmcFit(simonData, nTrl = 5000) plot(resTh, simonData)# Example 1 resTh <- dmcFit(flankerData, nTrl = 5000) plot(resTh, flankerData) plot(resTh, flankerData, figType = "deltaErrors") # Example 2 resTh <- dmcFit(simonData, nTrl = 5000) plot(resTh, simonData)
Plot delta function from multiple dmc simulations (i.e., dmcSims).
## S3 method for class 'dmclist' plot( x, ylim = NULL, xlim = NULL, figType = "delta", xlab = "Time [ms]", ylab = expression(paste(Delta, "Time [ms]")), xylabPos = 2, col = c("black", "lightgrey"), lineType = "l", legend = TRUE, legend.parameters = list(), ... )## S3 method for class 'dmclist' plot( x, ylim = NULL, xlim = NULL, figType = "delta", xlab = "Time [ms]", ylab = expression(paste(Delta, "Time [ms]")), xylabPos = 2, col = c("black", "lightgrey"), lineType = "l", legend = TRUE, legend.parameters = list(), ... )
x |
Output from dmcSims |
ylim |
ylimit for delta plot |
xlim |
xlimit for delta plot |
figType |
delta (default), deltaErrors |
xlab |
x-label |
ylab |
y-label |
xylabPos |
x/y label position |
col |
color range start/end color |
lineType |
line type ("l", "b", "o") for delta plot |
legend |
TRUE/FALSE Show legend |
legend.parameters |
list |
... |
pars for plot |
Plot (no return value)
# Example 1 params <- list(amp = seq(20, 30, 2)) dmc <- dmcSims(params) plot(dmc, col = c("red", "green"), legend.parameters = list(x = "topright", ncol=2)) # Example 2 params <- list(amp=c(10, 20), tau = c(20, 40), drc = c(0.2, 0.6), nTrl = 50000) dmc <- dmcSims(params) plot(dmc, col=c("green", "blue"), ylim = c(-10, 120), legend.parameters=list(ncol=2))# Example 1 params <- list(amp = seq(20, 30, 2)) dmc <- dmcSims(params) plot(dmc, col = c("red", "green"), legend.parameters = list(x = "topright", ncol=2)) # Example 2 params <- list(amp=c(10, 20), tau = c(20, 40), drc = c(0.2, 0.6), nTrl = 50000) dmc <- dmcSims(params) plot(dmc, col=c("green", "blue"), ylim = c(-10, 120), legend.parameters=list(ncol=2))
Plot results from the output of dmcObservedData. The plot can be an overall summary, or individual plots (rtCorrect, errorRate, rtErrors, cdf, caf, delta, deltaErrors, all).
## S3 method for class 'dmcob' plot( x, figType = "summary", subject = NULL, labels = c("Compatible", "Incompatible"), cols = c("green", "red"), errorBars = FALSE, errorBarType = "sd", ylimRt = NULL, ylimErr = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, ... )## S3 method for class 'dmcob' plot( x, figType = "summary", subject = NULL, labels = c("Compatible", "Incompatible"), cols = c("green", "red"), errorBars = FALSE, errorBarType = "sd", ylimRt = NULL, ylimErr = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, ... )
x |
Output from dmcObservedData |
figType |
summary, rtCorrect, errorRate, rtErrors, cdf, caf, delta, deltaErrors, deltaER, all |
subject |
NULL (aggregated data across all subjects) or integer for subject number |
labels |
Condition labels c("Compatible", "Incompatible") default |
cols |
Condition colours c("green", "red") default |
errorBars |
TRUE(default)/FALSE Plot errorbars |
errorBarType |
sd(default), or se |
ylimRt |
ylimit for Rt plots |
ylimErr |
ylimit for error rate plots |
xlimCDF |
xlimit for CDF plot |
ylimCAF |
ylimit for CAF plot |
cafBinLabels |
TRUE/FALSE |
ylimDelta |
ylimit for delta plot |
xlimDelta |
xlimit for delta plot |
xlabs |
TRUE/FALSE |
ylabs |
TRUE/FALSE |
xaxts |
TRUE/FALSE |
yaxts |
TRUE/FALSE |
xylabPos |
2 |
resetPar |
TRUE/FALSE Reset graphical parameters |
legend |
TRUE/FALSE (or FUNCTION) plot legend on each plot |
... |
additional plot pars |
Plot (no return value)
# Example 1 (real dataset) plot(flankerData) plot(flankerData, errorBars = TRUE, errorBarType = "se") plot(flankerData, figType = "delta") plot(flankerData, figType = "caf") # Example 2 (real dataset) plot(simonData) plot(simonData, errorBars = TRUE, errorBarType = "se") plot(simonData, figType = "delta", errorBars = TRUE, errorBarType = "sd") # Example 3 (simulated dataset) dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(420, 100, 80), "Comp_incomp" = c(470, 100, 95)), Error = list("Comp_comp" = c(5, 3, 2, 1, 2), "Comp_incomp" = c(15, 8, 4, 2, 2))) datOb <- dmcObservedData(dat) plot(datOb, errorBars = TRUE, errorBarType = "sd") # Example 4 (simulated dataset) dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(420, 100, 150), "Comp_incomp" = c(470, 100, 120)), Error = list("Comp_comp" = c(5, 3, 2, 1), "Comp_incomp" = c(15, 8, 4, 2))) datOb <- dmcObservedData(dat, nCAF = 4) plot(datOb)# Example 1 (real dataset) plot(flankerData) plot(flankerData, errorBars = TRUE, errorBarType = "se") plot(flankerData, figType = "delta") plot(flankerData, figType = "caf") # Example 2 (real dataset) plot(simonData) plot(simonData, errorBars = TRUE, errorBarType = "se") plot(simonData, figType = "delta", errorBars = TRUE, errorBarType = "sd") # Example 3 (simulated dataset) dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(420, 100, 80), "Comp_incomp" = c(470, 100, 95)), Error = list("Comp_comp" = c(5, 3, 2, 1, 2), "Comp_incomp" = c(15, 8, 4, 2, 2))) datOb <- dmcObservedData(dat) plot(datOb, errorBars = TRUE, errorBarType = "sd") # Example 4 (simulated dataset) dat <- createDF(nSubjects = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp"))) dat <- addDataDF(dat, RT = list("Comp_comp" = c(420, 100, 150), "Comp_incomp" = c(470, 100, 120)), Error = list("Comp_comp" = c(5, 3, 2, 1), "Comp_incomp" = c(15, 8, 4, 2))) datOb <- dmcObservedData(dat, nCAF = 4) plot(datOb)
Plot delta results from the output of dmcObservedData. The plot can be an overall rtCorrect, errorRate, rtErrors, cdf, caf, delta, deltaErrors, deltaER, or all of the previous plots.
## S3 method for class 'dmcobs' plot( x, figType = "all", subject = NULL, labels = c("Compatible", "Incompatible"), cols = c("black", "gray"), ltys = c(1, 1), pchs = c(1, 1), errorBars = FALSE, errorBarType = "sd", ylimRt = NULL, ylimErr = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, legend.parameters = list(), ... )## S3 method for class 'dmcobs' plot( x, figType = "all", subject = NULL, labels = c("Compatible", "Incompatible"), cols = c("black", "gray"), ltys = c(1, 1), pchs = c(1, 1), errorBars = FALSE, errorBarType = "sd", ylimRt = NULL, ylimErr = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, legend.parameters = list(), ... )
x |
Output from dmcObservedData |
figType |
rtCorrect, errorRate, rtErrors, cdf, caf, delta, deltaErrors, deltaER, all |
subject |
NULL (aggregated data across all subjects) or integer for subject number |
labels |
Condition labels c("Compatible", "Incompatible") default |
cols |
Condition colours c("green", "red") default |
ltys |
Linetype see par |
pchs |
Symbols see par |
errorBars |
TRUE(default)/FALSE Plot errorbars |
errorBarType |
sd(default), or se |
ylimRt |
ylimit for Rt plots |
ylimErr |
ylimit for error rate plots |
xlimCDF |
xlimit for CDF plot |
ylimCAF |
ylimit for CAF plot |
cafBinLabels |
TRUE/FALSE |
ylimDelta |
ylimit for delta plot |
xlimDelta |
xlimit for delta plot |
xlabs |
TRUE/FALSE |
ylabs |
TRUE/FALSE |
xaxts |
TRUE/FALSE |
yaxts |
TRUE/FALSE |
xylabPos |
2 |
resetPar |
TRUE/FALSE Reset graphical parameters |
legend |
TRUE/FALSE |
legend.parameters |
list |
... |
additional plot pars |
Plot (no return value)
# Example 1 dat <- dmcCombineObservedData(flankerData, simonData) # combine flanker/simon data plot(dat, figType = "all", xlimDelta = c(200, 700), ylimDelta = c(-20, 80), cols = c("black", "darkgrey"), pchs = c(1, 2)) plot(dat, figType = "delta", xlimDelta = c(200, 700), ylimDelta = c(-20, 80), cols = c("black", "darkgrey"), pchs = c(1, 2), legend = TRUE, legend.parameters=list(x="topright", legend=c("Flanker", "Simon")))# Example 1 dat <- dmcCombineObservedData(flankerData, simonData) # combine flanker/simon data plot(dat, figType = "all", xlimDelta = c(200, 700), ylimDelta = c(-20, 80), cols = c("black", "darkgrey"), pchs = c(1, 2)) plot(dat, figType = "delta", xlimDelta = c(200, 700), ylimDelta = c(-20, 80), cols = c("black", "darkgrey"), pchs = c(1, 2), legend = TRUE, legend.parameters=list(x="topright", legend=c("Flanker", "Simon")))
Plot the simulation results from the output of dmcSim. The plot can be an overall summary, or individual plots (activation, trials, pdf, cdf, caf, delta, all). Plot type summary1 contains an activation plot, example individual trials, the probability distribution function (PDF), the cumulative distribution function (CDF), the conditional accuracy function (CAF) and delta plot. This requires that dmcSim is run with fullData = TRUE. Plot type summary2 contains only the PDF, CDF, CAF and delta plots and does not require that dmcSim is run with fullData = TRUE.
## S3 method for class 'dmcsim' plot( x, figType = "summary1", xlimActivation = NULL, ylimActivation = NULL, xlimTrials = NULL, ylimTrials = NULL, xlimPDF = NULL, ylimPDF = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, ylimRt = NULL, ylimErr = NULL, labels = c("Compatible", "Incompatible"), cols = c("green", "red"), errorBars = FALSE, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, ... )## S3 method for class 'dmcsim' plot( x, figType = "summary1", xlimActivation = NULL, ylimActivation = NULL, xlimTrials = NULL, ylimTrials = NULL, xlimPDF = NULL, ylimPDF = NULL, xlimCDF = NULL, ylimCAF = NULL, cafBinLabels = FALSE, ylimDelta = NULL, xlimDelta = NULL, ylimRt = NULL, ylimErr = NULL, labels = c("Compatible", "Incompatible"), cols = c("green", "red"), errorBars = FALSE, xlabs = TRUE, ylabs = TRUE, xaxts = TRUE, yaxts = TRUE, xylabPos = 2, resetPar = TRUE, legend = TRUE, ... )
x |
Output from dmcSim |
figType |
summary1, summary2, summary3, activation, trials, pdf, cdf, caf, delta, deltaErrors, deltaER, rtCorrect, rtErrors, errorRate, all |
xlimActivation |
xlimit for activation plot |
ylimActivation |
ylimit for activation plot |
xlimTrials |
xlimit for trials plot |
ylimTrials |
ylimit for trials plot |
xlimPDF |
xlimit for PDF plot |
ylimPDF |
ylimit for PDF plot |
xlimCDF |
xlimit for CDF plot |
ylimCAF |
ylimit for CAF plot |
cafBinLabels |
TRUE/FALSE |
ylimDelta |
ylimit for delta plot |
xlimDelta |
xlimit for delta plot (Default is 0 to tmax) |
ylimRt |
ylimit for rt plot |
ylimErr |
ylimit for er plot |
labels |
Condition labels c("Compatible", "Incompatible") default |
cols |
Condition colours c("green", "red") default |
errorBars |
TRUE/FALSE |
xlabs |
TRUE/FALSE |
ylabs |
TRUE/FALSE |
xaxts |
TRUE/FALSE |
yaxts |
TRUE/FALSE |
xylabPos |
2 |
resetPar |
TRUE/FALSE Reset graphical parameters |
legend |
TRUE/FALSE |
... |
additional plot pars |
Plot (no return value)
# Example 1 dmc = dmcSim(fullData = TRUE) plot(dmc) # Example 2 dmc = dmcSim() plot(dmc) # Example 3 dmc = dmcSim(tau = 120) plot(dmc) # Example 4 dmc = dmcSim() plot(dmc, figType = "all")# Example 1 dmc = dmcSim(fullData = TRUE) plot(dmc) # Example 2 dmc = dmcSim() plot(dmc) # Example 3 dmc = dmcSim(tau = 120) plot(dmc) # Example 4 dmc = dmcSim() plot(dmc, figType = "all")
Returns value(s) from a distribution appropriate to simulate reaction times. The distribution is a combined exponential and gaussian distribution called an exponentially modified Gaussian (EMG) distribution or ex-gaussian distribution.
rtDist(n = 10000, gaussMean = 600, gaussSD = 50, expRate = 200)rtDist(n = 10000, gaussMean = 600, gaussSD = 50, expRate = 200)
n |
Number of observations |
gaussMean |
Mean of the gaussian distribution |
gaussSD |
SD of the gaussian distribution |
expRate |
Rate of the exponential function |
double
# Example 1 x <- rtDist() hist(x, 100, xlab = "RT [ms]") # Example 2 x <- rtDist(n=2000, gaussMean=500, gaussSD=100, expRate=300) hist(x, 100, xlab = "RT [ms]")# Example 1 x <- rtDist() hist(x, 100, xlab = "RT [ms]") # Example 2 x <- rtDist(n=2000, gaussMean=500, gaussSD=100, expRate=300) hist(x, 100, xlab = "RT [ms]")
$summary –> Reaction time correct, standard deviation correct, standard error correct, percentage error, standard deviation error, standard error error, reaction time incorrect, standard deviation incorrect, and standard error incorrect trials for both compatible and incompatible trials
$caf –> Proportion correct for compatible and incompatible trials across 5 bins
$delta –> Compatible reactions times, incompatible mean reaction times, mean reaction times, incompatible - compatible reaction times (effect), and standard deviation + standard error of this effect across 19 bins
$data –> Raw data from simonData.txt + additional outlier column
simonDatasimonData
dmcob
Summary of the simulation results from dmcFit
## S3 method for class 'dmcfit' summary(object, digits = 2, ...)## S3 method for class 'dmcfit' summary(object, digits = 2, ...)
object |
Output from dmcFit |
digits |
Number of digits in the output |
... |
pars |
DataFrame
# Example 1 fitAgg <- dmcFit(flankerData, nTrl = 1000) summary(fitAgg)# Example 1 fitAgg <- dmcFit(flankerData, nTrl = 1000) summary(fitAgg)
Summary of the simulation results from dmcFitSubjectX
## S3 method for class 'dmcfit_subject' summary(object, digits = 2, ...)## S3 method for class 'dmcfit_subject' summary(object, digits = 2, ...)
object |
Output from dmcFitSubject |
digits |
Number of digits in the output |
... |
pars |
DataFrame
# Example 1 fitSubject <- dmcFitSubject(flankerData, nTrl = 1000, subjects = c(1:3)) summary(fitSubject)# Example 1 fitSubject <- dmcFitSubject(flankerData, nTrl = 1000, subjects = c(1:3)) summary(fitSubject)
Summary of the simulation results from dmcFit
## S3 method for class 'dmcfits' summary(object, digits = 2, ...)## S3 method for class 'dmcfits' summary(object, digits = 2, ...)
object |
Output from dmcFit |
digits |
Number of digits in the output |
... |
pars |
DataFrame
# Example 1 fitAggs <- dmcFit(list(flankerData, simonData), nTrl = 1000) summary(fitAggs)# Example 1 fitAggs <- dmcFit(list(flankerData, simonData), nTrl = 1000) summary(fitAggs)
Summary of the simulation results from dmcFitAgg
## S3 method for class 'dmcfits_subject' summary(object, digits = 2, ...)## S3 method for class 'dmcfits_subject' summary(object, digits = 2, ...)
object |
Output from dmcFitAgg |
digits |
Number of digits in the output |
... |
pars |
DataFrame
# Example 1 fitsSubject <- dmcFitSubject(list(flankerData, simonData), nTrl = 1000, subjects = c(1:3)) summary(fitsSubject)# Example 1 fitsSubject <- dmcFitSubject(list(flankerData, simonData), nTrl = 1000, subjects = c(1:3)) summary(fitsSubject)
Summary of the overall results from dmcSim
## S3 method for class 'dmcsim' summary(object, digits = 1, ...)## S3 method for class 'dmcsim' summary(object, digits = 1, ...)
object |
Output from dmcSim |
digits |
Number of digits in the output |
... |
pars |
DataFrame
# Example 1 dmc <- dmcSim() summary(dmc) # Example 2 dmc <- dmcSim(tau = 90) summary(dmc)# Example 1 dmc <- dmcSim() summary(dmc) # Example 2 dmc <- dmcSim(tau = 90) summary(dmc)