Log Modulus Transformation

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Log Modulus Transformation

I read a recent Blog at :

 

 

http://blogs.sas.com/content/iml/2014/07/14/log-transformation-of-pos-neg.html

 

related to the log-modulus transformation which was listed as:

 

L(x) = sign(x) * log(|x| + 1)

 

My question is that once you have the transformed value which is now positive how should that value be calculated for the normal scale.  I am using natural logs for my example

 

EG  x=0.24  ln(x)=-1.42

L(x)=-1 *Ln(|0.24|+1)=0.42

 

What sould I do if I have  values in that data set that do not give negative logs also add 1.0?

 

When  I want to return to the normal scale for this Ln value (0.42) ie ( exp) x,  what should be the appropriate value for x? 

 

I hope that I have interpreted the blog correctly.


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‎08-30-2016 08:33 AM
SAS Super FREQ
Posts: 3,476

Re: Log Modulus Transformation

I'm not sure what you mean by the "normal scale"?  What are you trying to accomplish?

 

The log-modulus transformation is one of many transformations that can be used for a variety of purposes, including visualizing data that spans many orders of magnitude, such as {-10000, -10, -0.01, 0.1, 100, 1000}.

 

Some other transformations (such as log() or sqrt()) are sometimes used as normalizing transformations. You can apply a normalizing transformation with the data are lognormally distributed (use log() ) or are distributed like x^2 (use sqrt()).

 

Perhaps you are asking "how can I undo the log-modulus transformation"?

The inverse transformation is 

z = sign(y) ( exp(abs(y)) - 1 )

For example, if your original data X are normally distributed and you apply the log-modulus transformation to get Y, then you can apply the inverse transformation to recover X.

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‎08-30-2016 08:33 AM
SAS Super FREQ
Posts: 3,476

Re: Log Modulus Transformation

I'm not sure what you mean by the "normal scale"?  What are you trying to accomplish?

 

The log-modulus transformation is one of many transformations that can be used for a variety of purposes, including visualizing data that spans many orders of magnitude, such as {-10000, -10, -0.01, 0.1, 100, 1000}.

 

Some other transformations (such as log() or sqrt()) are sometimes used as normalizing transformations. You can apply a normalizing transformation with the data are lognormally distributed (use log() ) or are distributed like x^2 (use sqrt()).

 

Perhaps you are asking "how can I undo the log-modulus transformation"?

The inverse transformation is 

z = sign(y) ( exp(abs(y)) - 1 )

For example, if your original data X are normally distributed and you apply the log-modulus transformation to get Y, then you can apply the inverse transformation to recover X.

Frequent Contributor
Posts: 83

Re: Log Modulus Transformation

The inverse transform was what I was seeking.  Thanks for the clarification.

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