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PODeconvolution with R (decon and deamer package)
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copied!<p>I have a model of the form: y = x + noise. I know the distribution of 'y' and of the noise and would like to have the distribution of 'x'. So I tried to deconvolve the distributions with R. I found 2 packages (decon and deamer) and I thought both methods should make more or less the same but I don't understand why deconvoluting with DeconPdf gives me a something like a normal distribution and deconvoluting with deamerKE gives me a uniform distribution. Here is an example code:</p> <pre><code>library(fitdistrplus) # for rweibull library(decon) # for DeconPdf library(deamer) # for deamerKE set.seed(12345) y <- rweibull(10000, shape=5.780094, scale=0.00204918) noise <- rnorm(10000, mean=0.002385342, sd=0.0004784688) sdnoise <- sd(noise) est <- deamerKE(y, noise.type="Gaussian", mean(noise), sigma=sdnoise) plot(est) estDecon <- DeconPdf(y, sdnoise, error="normal", fft=TRUE) plot(estDecon) </code></pre> <hr> <p><strong>Edit</strong> (in response to <a href="https://stackoverflow.com/a/12464936/1635321">Julien Stirnemann</a>):</p> <p>I am not sure about re-parametrizing. My actual problem is: I have reaction time (RT) which theoretically can be described as f(RT) = g(discrimination time) + h(selection time), where f,g and h are can be transformations of those time values. I have "RT" and "discrimination time" values in my dataset. And I am interested in selection time or maybe h(selection time). With kernel density estimation I found out that the weibull distribution fits the 1/RT values best, while normal distribution fits 1/(discrimination time) best. That is why I can write my problem as 1/RT = 1/(discrimination time) + h(selection time) or y = x + noise (where I considered the noise to be 1/(discrimination time)). Simulating those reaction times gave me the following distribution with the following parameters:</p> <pre><code>y <- rweibull(10000, shape=5.780094, scale=0.00204918) noise <- rnorm(10000, mean=0.002385342, sd=0.0004784688) </code></pre> <p>What do you mean with re-parametrizing? Using different values e.g. for the scale parameter? </p>

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