Definitely not. Categorizing into 5 levels takes 4 degrees of freedom and still assumes a piecewise flat relationship. Regression splines handle U-shapes as well as other smooth shapes, with typically < 4 d.f., and they provide better fits.
Frank Harrell On Sun, 21 Apr 2002 18:56:59 -0700 Jay Tanzman <[EMAIL PROTECTED]> wrote: > > > Frank E Harrell Jr wrote: > > > > [Note: The original post should have been to sci.stat.consult, not sci.stat.edu] > > > > Categorizing continuous variables to avoid a linearity assumption is always a >curious thing to do in my view. Is a piecewise flat relationship more realistic than >a linear one? > > > > Regression splines and other flexible approaches do away with the need for the >linearity assumption anyway. > > Well, yeah, I would say so. If the relationship is U-shaped, say, then > re-coding a continuous predictor variable into 5 categories, will provide a > better fit. > > I think, too, that one reason for preferring categorical predictors, at least in > my field, nutritional epidemiology, is that the shape of the relatinship may be > unknown a priori, and using categories results in a more flexible model. > > -Jay -- Frank E Harrell Jr Prof. of Biostatistics & Statistics Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences U. Virginia School of Medicine http://hesweb1.med.virginia.edu/biostat . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
