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With the help of other packages, I've estimated Drift Diffusion model parameters of my data. Now, I want to estimate the predicted (or actual) response times for each observation with the help of estimated parameters. By going through the manual I got a sense that ddiffusion function estimates (predicts) the actual response times, basically I used this code for my own purposes:

dd1<-ddiffusion(data$rt , data$resp,a=2.16,v=1.12,t0=0.36,z=0.51).

Does this function gives me actual (predicted) response time for each observation?

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Not very familiar with this package, but per manual (bold and italics added):

ddiffusion gives the density, pdiffusion gives the distribution function, qdiffusion gives the quantile function (i.e., predicted RTs), and rdiffusion generates random response times and decisions (returning a data.frame with columns rt (numeric) and response (factor)).

So, it looks like if you want predicted reaction times, you should use the qdiffusion function (but note that it is a quantile function).

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  • $\begingroup$ mfloren, thanks for the contribution. When I run qdiffusion returns, for instance, 2.27 when the observed response time is 0.62. It doesn't make sense, as predicted RT cannot be higher than the observed RT. $\endgroup$ – Samir Jul 22 '17 at 16:16
  • $\begingroup$ @Samir The qdiffusion gives quantiles of the predicted values based off of "minimizing the absolute difference between desired probability and the value returned from pdiffusion using optimize." Have you gotten a chance to look through the examples in the documentation? That is where all of this information is coming from. $\endgroup$ – mfloren Jul 22 '17 at 16:40
  • $\begingroup$ Yes, I am constantly reading the manual. Apparently, I have to spend more time on it. Thanks $\endgroup$ – Samir Jul 22 '17 at 16:41
  • $\begingroup$ @Samir You're welcome. I am not an expert of this package by any means, and perhaps others can give a more satisfactory answer. I'll update mine to include that. $\endgroup$ – mfloren Jul 22 '17 at 16:42
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The functions estimate the parameters of the distribution. If I understand you correctly, you want to make individual predictions - these require a predictor variable in the sense of a regression model. (sorry for the late response, I just stumbled across the question)

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  • $\begingroup$ Thanks - appreciate your contribution! $\endgroup$ – Samir Oct 27 at 2:41

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