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    <p>Following your second post: I'm not sure I fully understand your problem. However, from what I understand, 2 possibilities:</p> <p>1) without using any transformation function, selection time = RT - discrimination time. If RT and discrimination time are both observed on each individual of your dataset, selection time is deterministically known - and this has nothing to do with deconvolution.</p> <p>2) If RT is observed in one i.i.d. sample and selection time in another <strong>independant</strong> sample, then yes, the only way out is to consider deconvolution density estimation. However, although you have made some parametric assumptions using fitting methods, you truly do not know the densities of RT or DT. Considering DT as noise, your problem is: RT = ST + noise, with an auxiliary sample of i.i.d. noise given by your sample of discrimination time. You wish to estimate the density of ST which is unobserved. The only package that can perform deconvolution in this situation is deamer (as far as I know) with the deamerSE function. If I stated your problem correctly you should look at the examples in the manual. I would also recommend using the raw data without transformations (at least in a first analysis). An example:</p> <pre><code>deamerSE(RT, errors=DT) </code></pre> <p>Here again your error is not centered (it is positive), therefore you will have to adjust from and to, to account for the shift the error has generated...this also is in the examples of the deamer manual.</p> <p>Best, Julien Stirnemann</p>
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    1. USjulien stirnemann
    1. USjulien stirnemann
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    1. COYes, we can write my problem as RT = ST + noise. This was also my first idea. I already tried to estimate ST with deamerSE as you described. But this gave me a weird [distribution](http://img6.imagebanana.com/img/w6y0p8ka/Rplot.png). I have observations from 0.2 seconds to 1.4 seconds (using milliseconds instead of seconds didn't work). So I tried to simulate observations (and also tried some transformations of those), to figure out if I always get such a weird density estimation. Is this weird distribution the result of using non-centered errors? And how do i coose the from and to correctly?
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    2. COI could check if you can give an accurate simulation model for untransformed ST and noise (say using densities on R+) along with sample sizes?
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      1. USjulien stirnemann
    3. COHere is an simulation model which I think suits best: [link](http://pastebin.com/nc2mWF1L). By the way i found a nice descripton of my Problem [here](http://stats.stackexchange.com/questions/33703/how-could-i-extract-the-distribution-of-one-rv-when-given-a-set-of-sums-of-two-r). It's exactly the same situation I have.
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