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Michal Kvasnicka

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Professional Interests:: numerical analysis, mathematical modeling, high performance computing

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Comments and Ratings by Michal		View all 1 - 2 of 2
Updated	File	Comments	Rating
09 Jun 2009	Polyfitn N-d polynomial regression model Author: John D'Errico	John, could you add to the polyfitn package description file mentioned in your ReadMe file or any relevant reference on multidimensional polynomial regression. "In additions to these m-files, there are several other files of interest. For those who have an interest in understanding how polyfitn works (quite simple really) you should look to the file understanding_polyfitn.rtf, in the doc subdirectory." The underlying theory will be very helpful for me. Thanks in advance ...
09 Jun 2009	General Least Squares Regression Multi Dimensional Multivariable Least Squares Regression Author: Husam Aldahiyat	at STRB2 function is probably infinite while loop problem

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File	General Least Squares Regression
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Files Matching Michal's Watch List				View all 1 - 2 of 2
Updated	File	Tags	Downloads (last 30 days)	Comments	Rating
09 Sep 2009	General Least Squares Regression Multi Dimensional Multivariable Least Squares Regression Author: Husam Aldahiyat	polynomial, regression, least, squares, generalized, multidimensional	325	4	3.0 3.0 \| 2 ratings
20 Mar 2008	Polyfitn N-d polynomial regression model Author: John D'Errico	surface, linear regression, polynomial, fit, modeling, interpolation	322	28	4.52632 4.5 \| 21 ratings

Comments and Ratings Matching Michal's Watch List			1-5 of 35
Date	File	Comment by	Comments	Rating
22 Oct 2009	Polyfitn N-d polynomial regression model	John D'Errico	James, You don't need every possible combination of (x,y,z) to be known to estimate a model, certainly not a first order model like this. To estimate the 3 unknown parameters in this model, in theory, you need at least 3 data points at a minimum. In practice, you won't get very good estimates of those parameters with only 3 data points. In the past, I often had clients ask me how much data they needed to obtain. After all, data is often scarce, expensive, hard to obtain. It costs money, and time. You can't have too much data, at least if it is good data. In practice, if you end up with 10 or 20 points to estimate these three parameters, that will (probably) be entirely adequate. More data will give you higher quality estimates. Even 6 points, carefully chosen, if the noise is low enough, will often be adequate. Only you know the quality of your data, and how much you have, as well as how accurately you need the model to be estimated. If you have further questions, I can only suggest you send me an e-mail, with your data, and any other information you have on the problem. John	Comment only
21 Oct 2009	Polyfitn N-d polynomial regression model	James	Hi John, I am new to all this stuff. actually to MATLAB itself. I have 2D data I want to fit (to a 2-variable function) with 3 parameters: z = A + Bx +Cy. But that is scarce matrix, i.e. with holes. I do not have data (or z if you prefer) for each and every combination of x and y, but only for a sall number of them. Any way around this ? I appreciate your help in that matter. James	Comment only
11 Oct 2009	Polyfitn N-d polynomial regression model	Andreas	Great work ! Thank you very much. It is really easy to use, allows a lot of user specifications and is quite fast imho.	5
27 Aug 2009	Polyfitn N-d polynomial regression model	John D'Errico	Assuming that you have an array, that contains only w(x,y,z) for each element of the array, use ndgrid to generate the combinations of (x,y,z). Then call polyfitn with the data.	Comment only
27 Aug 2009	Polyfitn N-d polynomial regression model	Chao Xu	Hallo John, Nice work! I am a newbie in matlab and still got some questions about 3d fit. I have a 3d dataset F(128,128,16) and would like to get the polynomial function F(x,y,z), how should I do it? thanks!	5

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