Box-Cox power transformation for Linear Models

Version 1.1.0.0 (5.58 KB) by Hovav Dror
A utility helping finding lamda to transform y-->y^lamda to best fit multiple linear regression ...
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Updated 31 Mar 2016

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Helps choose a Box-Cox power transformation for a multivariate linear regression.
Assume you are looking at the residuals of [b,bint,r] = regress(y,X) and it seems a transformation is in place. Use:
boxcoxlm(y,X) to find the best lambda for a Box-Cox power transformation (y^lambda, or log(y) for lambda=0)

The function will also plot the Maximum Log-Likelihood as a function of lambda, and a 95% confidence region for the best value of lambda

More control can be obtained using:

[LambdaHat,LambdaInterval]=boxcoxlm(y,X,PlotLogLike,LambdaValues,alpha)

which allows ommiting the plot, a different region or precision, and a different alpha value for the confidence interval

Cite As

Hovav Dror (2024). Box-Cox power transformation for Linear Models (https://www.mathworks.com/matlabcentral/fileexchange/10419-box-cox-power-transformation-for-linear-models), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R14SP3
Compatible with any release
Platform Compatibility
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Version Published Release Notes
1.1.0.0

Refresh to provide a BSD License
1.0.0.0

minor improvements: an optional output argument with the confidence interval, more examples, out of range flag