Hi all, This is more a question in statistics, but I hope to get also the R practice for my question: I have an ancova model where the response variable is flowering (plant has a flower = 1, no flower = 0). The explanatory variables are leaf length, leaf thick (both continuous variables), and soil type (factorial with three levels): > model<-glm(flower~(thick+length)*soil,family="binomial") >summary(aov(model)) In the aov summary I find a significant effect of all variables, and a significant interaction between thick and soil, so I want to explore this interaction after "cleaning" the effect of length. I thought of two possible ways to extract the residuals: > res.thick<-resid(update(model,~.-thick-soil-thick:soil)) or: > res.thick<-resid(glm(flower~length+length:soil,family="binomial")) I validated that the two methods give the same results. Anyhow, now I want to compare the effect of thick on flowering probability,separately for each soil. But the residuals extracted are not 0 or 1 anymore. Linear glm, such as > model1<-glm(res.thick1~thick*soil) doesn't seem to be right, and, moreover, I am interested in the estimated coefficients and their interpretation (say - plotting a meaningful graph). How can I get a logistic regression from residuals? Do I NEED logistic regression? How should I understand the coefficients I get in summary of the residuals model? How can I use the results of the residuals model for plotting the separate lines for the probability (logistic) curve? Thanks in advance Yuval -- Yuval Sapir, PhD Porter School of Environmental Studies Dept. of Plant Sciences Tel Aviv University, Tel Aviv, 69978 Israel Mobile:054-7203140; Lab: 03-6405877 http://www.yeruka.org.il/