Xiaohui Chen wrote:
step or stepAIC functions do the job. You can opt to use BIC by changing
the mulplication of penalty.
I think AIC and BIC are not only limited to compare two pre-defined
models, they can be used as model search criteria. You could enumerate
the information criteria for all possible models if the size of full
model is relatively small. But this is not generally scaled to practical
high-dimensional applications. Hence, it is often only possible to find
a 'best' model of a local optimum, e.g. measured by AIC/BIC.
Sure you can use them that way, and they may perform better than other
measures, but the resulting model will be highly biased (regression
coefficients biased away from zero). AIC and BIC were not designed to
be used in this fashion originally. Optimizing AIC or BIC will not
produce well-calibrated models as does penalizing a large model.
On the other way around, I wouldn't like to say the over-penalization of
BIC. Instead, I think AIC is usually underpenalizing larger models in
terms of the positive probability of incoperating irrevalent variables
in linear models.
If you put some constraints on the process (e.g., if using AIC to find
the optimum penalty in penalized maximum likelihood estimation), AIC
works very well and BIC results if far too much shrinkage
(underfitting). If using a dangerous process such as stepwise variable
selection, the more conservative BIC may be better in some sense, worse
in others. The main problem with stepwise variable selection is the use
of significance levels for entry below 1.0 and especially below 0.1.
Frank
X
Frank E Harrell Jr 写道:
Smita Pakhale wrote:
Hi Maria,
But why do you want to use forwards or backwards
methods? These all are 'backward' methods of modeling.
Try using AIC or BIC. BIC is much better than AIC.
And, you do not have to believe me or any one else on
this.
How does that help? BIC gives too much penalization in certain
contexts; both AIC and BIC were designed to compare two pre-specified
models. They were not designed to fix problems of stepwise variable
selection.
Frank
Just make a small data set with a few variables with
known relationship amongst them. With this simulated
data set, use all your modeling methods: backwards,
forwards, AIC, BIC etc and then see which one gives
you a answer closest to the truth. The beauty of using
a simulated dataset is that, you 'know' the truth, as
you are the 'creater' of it!
smita
--- Charilaos Skiadas <[EMAIL PROTECTED]> wrote:
A google search for "logistic regression with
stepwise forward in r" returns the following post:
https://stat.ethz.ch/pipermail/r-help/2003-December/043645.html
Haris Skiadas
Department of Mathematics and Computer Science
Hanover College
On May 28, 2008, at 7:01 AM, Maria wrote:
Hello,
I am just about to install R and was wondering
about a few things.
I have only worked in Matlab because I wanted to
do a logistic
regression. However Matlab does not do logistic
regression with
stepwiseforward method. Therefore I thought about
testing R. So my
question is
can I do logistic regression with stepwise forward
in R?
Thanks /M
______________________________________________
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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