User Portlet
| Discussions |
|---|
| Hello Henrik, Thanks for your reply. Sorry I did not read it earlier, sort of got disconnected from this post. I just opened this post because I saw the same thing you display for version 9, it works fine! (mine is in Linux). So, something got... |
| Thanks. it is good to know I was missing something simple. I had run into that scaling thing before, but did not anticipate its effect could be so drastic. It is in that borderline where you could almost call it a bug (at my work we would), but I can... |
| Jim, Thanks for your reply. I understand your argument. Trying to easily visualize the half-a-million points simply came up as something that would be helpful, and is really the tip of the iceberg. The data do not quite represent a random... |
| Hello, The link you provided mentions Manipulate. It is not clear whether the issue with Manipulate in Linux has been addressed. I have seen several reports that the buttons do not respond (the manipulation can only be effected with Autorun). ... |
| Jessica, Will this work for what you need: Integrate[x, {x, -2, 3}] 5/2 If you need faster numerical results then use the form: Integrate[x, {x, -2.0, 3.0}] 2.5 Best, OL. |
| Hello Alexandre, Could you make the code easier to read, or attach a notebook to your post? Also, I am curious, why G[x_][t_][nmax_], rather than G[x_, t_, nmax_]? Anyway, I doubt this could be the reason for the error. OL. |
| Hello Bianca, Thanks for your reply. There is certainly nothing wrong with doing this in FORTRAN; I have tried it and it works. However, my FORTRAN code needs to read the coefficients (a1, a2,...) from a file, and it also needs to read the dataset... |
| Use DistributionFitTest. Here is an example I ran with some random data (certainly does not fit the Burr distribution). data = RandomReal[{0, 1}, 400]; test = DistributionFitTest[data, SinghMaddalaDistribution[2, 2.5,... |
| I am far from an expert, but I know that as a rookie one can be far from optimal. On the other hand, it could also be that the problem you are trying to do is just too hard (it certainly looks that way). I will look at your notebooks some more. I was... |
| I am pretty certain (although the documentation is not very clear on this) that NonlinearModelFit does a least-squares fit, i.e., mymodel=NonlinearModelFit[{parameter and response data}, myform, variables] will look for the parameters that... |