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I want to model the probability of a binary variable x given some predictor, d. It needs two parameters:

  • One parameter that sets the "break point", at which p(x=1 | d) = 0.5.
  • One parameter that sets the "softness", i.e. how abruptly the probability changes around the break point.

(Sorry I'm sure there's standard terminology for this, but I'm a bit of a novice.)

A logistic regression model feels like the right approach, but the domain of d is [0, inf] (d is a distance metric). Is there a standard way to transform d to [-inf, inf] so a logistic regression can be used (e.g. f(x) = x-1/x)? Or is there another common model for this kind of scenario?

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    $\begingroup$ Is d a parameter of the logistic regression or a predictor used in logistic regression? It sounds like a predictor but you call it a parameter. If it is a predictor, then its domain is irrelevant. $\endgroup$ – Rob Hyndman Nov 10 '10 at 2:27
  • $\begingroup$ d is the predictor. Thanks for correcting me, I've updated the question. $\endgroup$ – redmoskito Nov 10 '10 at 2:29
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Logistic regression satisfies your requirements -- two parameters controlling the mid-point and the rate of change. But there is no restriction on the domain of predictors. You can just fit a logistic regression using d as it is.

However, if d is highly skewed (as it might be with that domain), then taking a log of d may be useful to prevent the fit being dominated by a few observations.

If d includes exact zeros, then you obviously can't take logs. In that case, if you still think you need a transformation, then I'd use an inverse hyperbolic sine transformation.

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  • $\begingroup$ BTW, I just took the log of d. d can include zeros, but the logistic function maps that to zero, which is what I wanted. $\endgroup$ – redmoskito Nov 10 '10 at 13:30

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