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
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.)
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;
Jonathan C. Lansey (2023). Power Law, Exponential and Logarithmic Fit (https://www.mathworks.com/matlabcentral/fileexchange/29545-power-law-exponential-and-logarithmic-fit), MATLAB Central File Exchange. Retrieved .
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Added new color option which lets you set the 'color' of both lines and markers with one parameter. Added robustness to NaN values.
Updated to use R2 as 'best fit' criterion rather than MSE
fixed 'skipbegin' feature functionality
Updated to include Mean Squared Error