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is there something like the curve fit platform where you can try different transformations of data to find the best linear fit?
On the curve fit platform you can try different types of curves and see which one is the best fit. I would like to be able to compare different data transformations to find the best linear fit. Is there a platform that can do this?
I am using JMP 17.0 on a MacBook Pro.
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Re: is there something like the curve fit platform where you can try different transformations of data to find the best linear fit?
Look at
Analyze=>Specialized Modeling=>Fit Curve
Look for the documentation at here
Help=>JMP Documentation Library=>Predictive and Specialized Modeling
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Re: is there something like the curve fit platform where you can try different transformations of data to find the best linear fit?
but I only want a linear fit. I want to try different data transformations to see which gives the best linear fit.
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Re: is there something like the curve fit platform where you can try different transformations of data to find the best linear fit?
An easy way to do that would be to create a separate new column for each of the transformations you want to use, and then use Fit Y by X platform and generate the Fit Line. You can then order the different regressions by Goodness of Fit
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Re: is there something like the curve fit platform where you can try different transformations of data to find the best linear fit?
I think you need to distinguish between linear functions and linear models. Polynomials are nonlinear functions but are linear models - Y=f(X,X2, X3, etc.) can be a linear function. Fit curve will fit such polynomials. It will also fit things that are not linear models. So, you can confine yourself to the polynomials for a start. Now, if you want logarithmic transformations, then I think you will need to follow txnelson's advice and create columns for these. Of course, a log transformation is a nonlinear function, but you can have a linear model in the logs of X and/or Y. No platform will automatically cover all potential transformations. I don't think you are insisting on only linear functions - least square regression will give you the best fitting linear function and there is no need to try others since the regression line (surface) is already the best fitting one.
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Re: is there something like the curve fit platform where you can try different transformations of data to find the best linear fit?
If you want to apply a Box Cox transformation before fitting the data, you can use the Fit Model platform and in the red triangle menu select
Then go to the Box-Cox menu and select
and you will get a fit for the transformed variable:
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Re: is there something like the curve fit platform where you can try different transformations of data to find the best linear fit?
Graph Builder is also a great tool for fitting in a transformed coordinate system.
e.g. change the axis scale to log - and fit a line instead of an exponential : )
there is a which which will bring such fits to a completely new level:
Graph Builder: disable Local Data Filter for specific plot types
Besides log transform the user can also select differnet transforms like probit scale for normal distributions - or he can specify his own transformation "manually" via JSL / Scale to Internal/External. A nice example can be found in the scripting index:
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Re: is there something like the curve fit platform where you can try different transformations of data to find the best linear fit?
A third option: go to Fit Y by X and use Fit Special ...
In the menu you can pick some common transformations for X and Y axis:
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Re: is there something like the curve fit platform where you can try different transformations of data to find the best linear fit?
thanks! this is the type of thing I was looking for.
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Re: is there something like the curve fit platform where you can try different transformations of data to find the best linear fit?
JMP provides an automated Box-Cox Transformation for the response in Fit Least Squares. This method generalizes the common transforms such as logarithms or roots.