[R] Discretize continous variables....

Frank E Harrell Jr f.harrell at vanderbilt.edu
Sat Jul 19 17:59:59 CEST 2008

Daniel Malter wrote:
> This time I agree with Rolf Turner. This sounds like homework. Whether or
> not, type
> ?ifelse
> in the R-prompt.
> Frank is right, it leads to a loss in information. However, I think it
> remains interpretable. Further, it is common practice in certain fields, and

I have to disagree.  It is easy to show that odds ratios so obtained are 
functions of the entire distribution of the predictor in question.  Thus 
they do not estimate a scientific quantity (something that can be 
interpreted out of context).  For example if age is cut at 65 and one 
were to add to the sample several subjects aged 100, the >=65 : <65 odds 
ratio would change even if the age effect did not.

> it maybe a reasonable way to check whether mostly outliers in the X drive
> your results (although other approaches are available for that as well). The
> main underlying question however should be, do you have reason to expect
> that the response is different by the groups you create rather than in the
> numbers of the continuous variable. 

Regression splines can help.  Sometimes the splines are stated in terms 
of the cube root of the predictor to avoid excess influence.


> Regarding question 2: I thought you mean that you want to reduce the number
> of levels (say 4) to a smaller number of levels (say 2) for one of your
> independent variables (i.e. one of the Xs), not Y. This makes sense only, if
> there is any good conceptual reason to group these categories - not just to
> get significance.
> Best,
> Daniel
> Frank E Harrell Jr wrote:
>> milicic.marko wrote:
>>> Hi R helpers,
>>> I'm preparing dataset to fir logistic regression model with lrm(). I
>>> have various cointinous and discrete variables and I would like to:
>>> 1. Optimaly discretize continous variables (Optimaly means, maximizing
>>> information value - IV for example)
>> This will result in effects in the model that cannot be interpreted and 
>> will ruin the statistical inference from the lrm.  It will also hurt 
>> predictive discrimination.  You seem to be allergic to continuous
>> variables.
>>> 2. Regroup discrete variables to achieve perhaps smaller number of
>>> level and better information value...
>> If you use the Y variable to do this the same problems will result. 
>> Shrinkage is a better approach, or using marginal frequencies to combine 
>> levels.  See the "pre-specification of complexity" strategy in my book 
>> Regression Modeling Strategies.
>> Frank
>>> Please suggest if there is some package providing this or same
>>> functionality for discretization...
>>> if there is no package plese suggest how to achieve this.

Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University

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