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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