I'm running R 2.10.1 with mboost 2.0 in order to build predictive models . I am performing prediction on a binomial outcome, using a linear function (glmboost). However, I am running into some confusion regarding centering. (I am not aware of an mboost-specific mailing list, so if the main R list is not the right place for this topic, please let me know.)
The boost_control() function allows for the choice between center=TRUE and center=FALSE. If I select center=FALSE, I am able to interpret the coefficients just like those from standard logistic regression. However, if I select center=TRUE, this is no longer the case. In theory and in practice with my data, centering improves the predictions made by the model, so this is an issue worth pursuing for me. Below is output from running the exact same data in exactly the same way, only differing by whether the "center" bit is flipped or not: Output with center=TRUE: [(Intercept)] => -0.04543632 [painscore] => 0.007553608 [Offset] => -0.546520621809327 Output with center=FALSE: [(Intercept)] => -0.989742 [painscore] => 0.001342585 [Offset] => -0.546520621809327 The mean of painscore is 741. It seems to me that for center=FALSE, mboost should modify the intercept by subtracting 741*0.007553608 from it (thus intercept should = -11.285). If I manually do this, the output is credible, and in the ballpark of that given by other methods (e.g., lrm or glm with a Binomial link function). If I don't do this, then the inverse logistic interpretation of the output is off by orders of magnitude. In the end, with "center=TRUE", and I want to make a prediction based on the coefficients returned by mboost, the results only make sense if I manually rescale my independent variables prior to making a prediction. Is this the desired behavior, or am I doing something wrong? Many thanks. ______________________________________________ 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.
