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

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

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

CC BY-SA 3.0
2017-07-22T16:16:17.237Z
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.
Samir
CC BY-SA 3.0
2017-07-22T16:40:41.693Z
@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.
mflo-ByeSE
CC BY-SA 3.0
2017-07-22T16:41:12.077Z
Yes, I am constantly reading the manual. Apparently, I have to spend more time on it. Thanks
Samir
CC BY-SA 3.0
2017-07-22T16:42:35.637Z
@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.
mflo-ByeSE
CC BY-SA 3.0
2017-07-22T15:43:02.017Z
2017-07-22T16:44:00.793Z
2017-07-22T16:44:00.793Z
mflo-ByeSE
<p>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)</p>

CC BY-SA 4.0
2019-10-27T02:41:38.850Z
Thanks - appreciate your contribution!
Samir
CC BY-SA 4.0
2019-10-25T10:32:24.273Z
2019-10-25T10:32:24.273Z
Mike Ehrmantraut
<p>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 <a href="https://cran.r-project.org/web/packages/rtdists/rtdists.pdf" rel="nofollow noreferrer">manual</a> I got a sense that <code>ddiffusion</code> function estimates (predicts) the actual response times, basically I used this code for my own purposes:</p>

<pre><code>dd1&lt;-ddiffusion(data$rt , data$resp,a=2.16,v=1.12,t0=0.36,z=0.51).
</code></pre>

<p>Does this function gives me actual (predicted) response time for each observation?</p>

<cognitive-modeling><software><reaction-time>
Does this rdists package provide actual (predicted) response time for each observation?
Samir
# Does this rdists package provide actual (predicted) response time for each observation?

(Asked by: username_0 on 2017-07-22)

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?

**Tags:** cognitive-modeling, software, reaction-time


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## 2 answers

### Answer by username_1 on 2017-07-22. Score: 1

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).

> **Comments:**
> **username_0**: 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.  
> **username_1**: @username_0 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.  
> **username_0**: Yes, I am constantly reading the manual. Apparently, I have to spend more time on it. Thanks  
> **username_1**: @username_0 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.  

---

### Answer by username_2 on 2019-10-25. Score: 0

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)

> **Comments:**
> **username_0**: Thanks - appreciate your contribution!  

---

