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.

Frank


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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