R/Cpp implementation of the diffusion process model (Diffusion Model for Conflict Tasks, DMC) presented in Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions (https://www.sciencedirect.com/science/article/pii/S0010028515000195)
CRAN https://cran.r-project.org/web/packages/DMCfun/index.html
The package is presented in the following paper:
https://www.sciencedirect.com/science/article/pii/S259026012100031X
# install version from CRAN install.packages("DMCfun") library(DMCfun) # install version from GitHub # install.packages("devtools") devtools::install_github("igmmgi/DMCfun")Basic Examples DMC Simulation
dmc <- dmcSim(fullData = TRUE) plot(dmc)
dmc$means Comp rtCor sdRtCor perErr rtErr sdRtErr 1 comp 440. 105. 0.633 479. 104. 2 incomp 459. 94.8 1.38 406. 95.2
dmc <- dmcSim(fullData = TRUE, tau = 150) plot(dmc)
dmc$means Comp rtCor sdRtCor perErr rtErr sdRtErr 1 comp 421. 90.4 0.259 504. 119. 2 incomp 484. 103. 2.37 425. 82.7
params <- list(tau = seq(20, 170, 10)) dmc <- dmcSims(params) plot(dmc, ncol = 2, col = c("red", "green"))Basic Examples DMC Fit: Real data using optimx (Nelder-Mead)
fit <- dmcFit(flankerData) # flanker data from Ulrich et al. (2015) plot(fit, flankerData)
summary(fit) amp tau drc bnds resMean resSD aaShape spShape sigm rmse 1 19.3 98.8 0.593 55.8 325. 28.4 2.26 2.84 4 8.91
fit <- dmcFit(simonData) # simon data from Ulrich et al. (2015) plot(fit, simonData)
amp tau drc bnds resMean resSD aaShape spShape sigm RMSE 1 16.91 47.77 0.59 56.68 317.16 33.43 1.68 3.53 4 10.01Basic Examples DMC Fit: Real data using DEoptim
fit <- dmcFitDE(flankerData) # flanker data from Ulrich et al. (2015) plot(fit, flankerData)
summary(fit) amp tau drc bnds resMean resSD aaShape spShape sigm RMSE 1 17.26 222.19 0.64 57.49 328.06 28.41 1.7 2.18 4 5.79
fit <- dmcFitDE(simonData) # simon data from Ulrich et al. (2015) plot(fit, simonData)
amp tau drc bnds resMean resSD aaShape spShape sigm RMSE 1 14.31 42.29 0.55 57.54 308.63 25.98 2.15 3.56 4 8.86
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