If you're going to use splines, another possibility is mgcv::gam (also part of standard R installation)
require(mgcv) gam(DV ~ s(IV), data= YourDataFrame, family=binomial) this has the advantage that the complexity of the spline is automatically adjusted/selected by the fitting algorithm (although occasionally you need to use s(IV,k=something_bigger) to adjust the default *maximum* complexity chosen by the code) On Sun, Jan 11, 2015 at 5:23 PM, Marc Schwartz <marc_schwa...@me.com> wrote: > >> On Jan 11, 2015, at 4:00 PM, Ben Bolker <bbol...@gmail.com> wrote: >> >> Stanislav Aggerwal <stan.aggerwal <at> gmail.com> writes: >> >>> >>> I have the following problem. >>> DV is binomial p >>> IV is quantitative variable that goes from negative to positive values. >>> >>> The data look like this (need nonproportional font to view): >> >> >> [snip to make gmane happy] >> >>> If these data were symmetrical about zero, >>> I could use abs(IV) and do glm(p >>> ~ absIV). >>> I suppose I could fit two glms, one to positive and one to negative IV >>> values. Seems a rather ugly approach. >>> >> >> [snip] >> >> >> What's wrong with a GLM with quadratic terms in the predictor variable? >> >> This is perfectly respectable, well-defined, and easy to implement: >> >> glm(y~poly(x,2),family=binomial,data=...) >> >> or y~x+I(x^2) or y~poly(x,2,raw=TRUE) >> >>> (To complicate things further, this is within-subjects design) >> >> glmer, glmmPQL, glmmML, etc. should all support this just fine. > > > As an alternative to Ben's recommendation, consider using a piecewise cubic > spline on the IV. This can be done using glm(): > > # splines is part of the Base R distribution > # I am using 'df = 5' below, but this can be adjusted up or down as may be > apropos > require(splines) > glm(DV ~ ns(IV, df = 5), family = binomial, data = YourDataFrame) > > > and as Ben's notes, is more generally supported in mixed models. > > If this was not mixed model, another logistic regression implementation is in > Frank's rms package on CRAN, using his lrm() instead of glm() and rcs() > instead of ns(): > > # after installing rms from CRAN > require(rms) > lrm(DV ~ rcs(IV, 5), data = YourDataFrame) > > > Regards, > > Marc Schwartz > > ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
