Probabilities can only be even approximately linearly related to a continuous predictor variable for a limited range, otherwise the model will predict probabilities below 0 or above 1.
At some point, they have to tail off... unless you are modelling something trivial like 'probability of being above the x'th quantile...' But there's no particular reason your logistic regression has to be based on a linear predictor scale. Take logs or otherwise transform the predictor if that is justified by the underlying process or the data? Steve E >>> <[EMAIL PROTECTED]> 11/15/07 8:03 PM >>> I was just curious if anyone knew of an alternative model to logistic regression where the probabilities seems pretty linear to the predictor rather than having that S shape that probit and logit assume. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
