Dear ronggui, There are several approaches you can take, one of which is to fit a GAM and simply look to see whether the relationships appear linear on the logit scale. As well, you could compare the fit of the GAM with semiparametric models in which each smooth term in turn is replaced by a linear term; see ?anova.gam in the mcgv or gam package and the on-line appendix on nonparametric regression to my R and S-PLUS Companion to Applied Regression (at http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/appendix-nonparametri c-regression.pdf, and slightly out of date).
Another approach is to fit the linear logit model with glm() and examine component+residual (partial-residual) plots via the cr.plots() function or the ceres.plots() function, both in the car package. If nonlinearity in, say, x is correctable by a power transformation, you can get an approximate score test for the need to transform x by adding the "constructed variable" I(x*log(x)) to the model and examining its Wald statistic; an added-variable plot (av.plots in car) for the constructed variable shows leverage and influence on the decision to transform x. You can also compute a suggested power transformation as p = 1 - b/g, where b is the coefficient of x in the *original* model and g that of the constructed variable. Details are in the R and S-PLUS Companion. Some further examples are in lecture notes at http://socserv.socsci.mcmaster.ca/jfox/Courses/soc740/lecture-11.pdf. If x is quantitative but discrete, refitting the logit model replacing x with as.factor(x) and comparing via anova() to the original model gives a test of nonlinearity. I hope this helps, John -------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox -------------------------------- > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of ronggui > Sent: Friday, April 01, 2005 10:19 PM > To: r-help@stat.math.ethz.ch > Subject: [R] using GAM to assess the linearity in logistic regression > > as agresti(2002) points out that we had better to screen the > data to see if the the logit(pi) and the predictor has linear > realtionship in logistic regressin.and i find some materials > in MASS and the refernce of s-plus.but it is a bit simple > and i can not exactly master the means to assess the > linearity in logistic regression. so anyone suggest some materials? > > i am not familiar with GAM,but i think thers maybe some > materials can let me use GAM to assess the linearity in > logistic regression without master GAM model. is it right? > > thank you! > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html