# Power Law, Exponential and Logarithmic Fit

버전 1.5.0.0 (20.9 KB) 작성자:
Finds and plots the linear fit to some data points when plotted on a log scale.
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업데이트 날짜: 2014/8/22

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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;

### 인용 양식

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. 검색됨 .

개발 환경: R2010b
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버전 게시됨 릴리스 정보
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.

1.4.0.0

Updated to use R2 as 'best fit' criterion rather than MSE

1.3.0.0

fixed 'skipbegin' feature functionality

1.2.0.0

Updated to include Mean Squared Error

1.0.0.0