Power Law, Exponential and Logarithmic Fit
logfit(X,Y,graphType), where X is a vector and Y is a vector or a
matrix will plot the data with the axis scaling determined
by graphType as follows: graphType-> xscale, yscale
loglog-> log, log
logx -> log, linear
logy -> linear, log
linear -> linear, linear
A line is then fit to the scaled data in a least squares
sense.
See the 'notes' section below for help choosing a method.
logfit(X,Y), will search through all the possible axis scalings and
finish with the one that incurs the least error (with error
measured as least squares on the linear-linear data.)
Notes:
A power law relationship
[slope, intercept] = logfit(x,y,'loglog');
yApprox = (10^intercept)*x.^(slope);
An exponential relationship
[slope, intercept] = logfit(x,y,'logy');
yApprox = (10^intercept)*(10^slope).^x;
A logarithmic relationship
[slope, intercept] = logfit(x,y,'logx');
yApprox = (intercept)+(slope)*log10(x);
A linear relationship
[slope, intercept] = logfit(x,y,'linear');
yApprox = (intercept)+(slope)*x;
Cita come
Jonathan C. Lansey (2024). Power Law, Exponential and Logarithmic Fit (https://www.mathworks.com/matlabcentral/fileexchange/29545-power-law-exponential-and-logarithmic-fit), MATLAB Central File Exchange. Recuperato .
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Versione | Pubblicato | Note della release | |
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1.5.0.0 | Added new color option which lets you set the 'color' of both lines and markers with one parameter. Added robustness to NaN values. |
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1.4.0.0 | Updated to use R2 as 'best fit' criterion rather than MSE |
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1.3.0.0 | fixed 'skipbegin' feature functionality |
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1.2.0.0 | Updated to include Mean Squared Error |
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1.0.0.0 |