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Re: st: Knot optimized logistic regression

From   Dan MacNulty <[email protected]>
To   [email protected]
Subject   Re: st: Knot optimized logistic regression
Date   Fri, 29 Jan 2010 11:08:05 -0800

The location of the knot is the key research question. My initial approach was to use AIC to select the best-fit model from among a set of models each with a different fixed knot. However, a reviewer has criticized this approach, arguing that I should have estimated the knot as a parameter because it affords a better measure of uncertainty about the knot location.


Maarten buis wrote:
--- On Fri, 29/1/10, Dan MacNulty wrote:
Thanks very much for sharing your code. There is one added
twist: my data are clustered within subject, so I'm
estimating a random-intercept logistic spline regression. I
assume the addition of a random effect will make knot
estimation even more difficult, correct?

That is certainly not going to make things easier. How crucial
is it to estimate the location of the knot? There are cases
where the location of the knot/turning point is the key
research question, but in most cases we use splines to represent
a non-linear effect, in which case you are much better of by just choosing some reasonable locations.

-- Maarten

Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen

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