[Scilab-users] Convert x, y, z data into a z=f(x,y) function

Mon Jul 1 11:24:42 CEST 2013

Hi, 

so regarding the data.. 

it was an experiment related to algal biomass yield (g/m&sup2;) ("z") according to time (0-25days, the "x"-data) and light intensity (25-1000µmol*m^-1*s^-1, the "y" values). Increase in biomass might have been exponential right at the beginning, but with increasing time I guess it turned out to be linear... 

does anybody have a hint about the model I could use?:) 

Gesendet: Montag, 01. Juli 2013 um 11:14 Uhr 
Von: "Mike Page [via Scilab / Xcos - Mailing Lists Archives]" <ml-node+s994242n4026925h99 at n3.nabble.com> 
An: Larissa <larikerstin at gmx.de> 
Betreff: Re: Convert x, y, z data into a z=f(x,y) function 

Hi, 

You are asking a question which in general has no answer.  There are an infinite number of models which can fit your data.  You need to find some possible candidate model forms based on physical properties and then try fitting to them.  You are probably looking for a fit which leaves residues which are Gaussian and mean zero (but that's not always true...). 

Try giving us a clue about where the data come from.  Maybe somebody has the physical background to suggest some models.  If not, try plotting the data and guessing a model from the shape.  To me the shape looks vaguely exponential or logarithmic, so maybe plotting as log-linear or log-log will give a clue. 

HTH, 

Mike. 

-----Original Message----- 
From: [hidden email]  [mailto: [hidden email] ]On Behalf Of Larissa Schultze 
Sent: 01 July 2013 09:43 
To: International users mailing list for Scilab. 
Subject: Re: [Scilab-users] Convert x, y, z data into a z=f(x,y) function 

Hello all, 

first of all, thanks a lot for your effort. I must say that I don't really have anyone to run to for asking about mathematical models - I could go to the mathematicians at the University, but I don't know anyone there and I barelly know where the institute is located... 

therefore I decided to insert my simplest table in here (it is actually very simple) - may be someone here knows which kind of mathematical model I could use? 

I have been searching for it in literature, but I don't seem to be in the right path... 

So attached you will find my data table as well as the scilab commands I used to create the respective graph. I mean, my data is already interpolated...shouldn't it be easy to get a function (z,x,y) out of it? 

I would be very very thankful for any help...I'm getting a bit desperate... 

best regards, 

Larissa 

Gesendet: Dienstag, 25. Juni 2013 um 11:09 Uhr 
Von: "CRETE Denis" < [hidden email] > 
An: "International users mailing list for Scilab." < [hidden email] > 
Betreff: Re: [Scilab-users] Convert x, y, z data into a z=f(x,y) function 

Hello, 

The general procedure for fitting data in the case of 2 variables is the following: 
// First define your mathematical model by changing the following line 
deff('z=MyFunction(x,y)', 'z=p(1)*x + p(2)*y + p(3)*x.*y'); 
// Store all experimental data in a single array ExD; X, Y, Z assumed to be 1 x NZ vectors 
ExD=[X;Y;Z]; 
// Define the error function (to be minimized with respect to the parameters p) 
deff('erro=G(p,ExD)','x=ExD(1),y=ExD(2), z=ExD(3), erro=z-MyFunction(x,y)') 
// Fit experimental data contained in W 
// The column vector p0 is an initial guess of the values for the parameters of your Model 
[p,err]=datafit(G,ExD,p0) 
// you can check values generated with 
MyFunction(X,Y) 

HTH 
Denis 

-----Message d'origine----- 
De : [hidden email]  [mailto: [hidden email] ] De la part de Dang, Christophe 
Envoyé : mardi 25 juin 2013 10:20 
À : International users mailing list for Scilab. 
Objet : Re: [Scilab-users] Convert x, y, z data into a z=f(x,y) function 

Hello, 

De la part de Larissa 
Envoyé : mardi 25 juin 2013 09:52 

> I conducted an experiment and thus my results are composed of x,y,z 
> data, 
[...] 
> but I can't figure out how to get an equation "z=f(x,y)" out of it. 

This is more a math problem than a Scilab problem. 

You must have a mathematical model, i.e. a parametric formula, then you can adjust the parameters by regression (or maximum likehood). 

You may have theoretical models that derive from elementary assumptions 
-- you usually find such model in the bibliography --, or use a "nice model that fit the global shape" 
-- you may ask the math laboratory in your neighbourhood, this is usually polynomials, exponentials, statistical laws... 

So if you come to us with a parametric model, we will be able to help you. 

best regards. 

-- 
Christophe Dang Ngoc Chan 
Mechanical calculation engineer 

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