On Wed, 2007-12-05 at 22:33 -0800, Bin Yue wrote: > Dear all: > By comparing glmresult$y and model.response(model.frame(glmresult)), I > have found out which one is > set to be "TRUE" and which "FALSE".But it seems that to fit a logistic > regression , logit (or logistic) transformation has to be done before > regression. > Does anybody know how to obtain the transformation result ? It is hard > to settle down before knowing the actual process R works . I have read some > books and the "?glm" help file , but what they told me was not sufficient. > Best wishes , > Bin Yue
Bin, I may be mis-interpreting your follow up query, but here goes: You have presumably created a logistic regression model. The resultant model object is called 'glmresult'. If you use: fitted(glmresult) it will return the fitted predicted values on a probability scale (0 - 1) for the original set of data that you used. You can also use: predict(glmresult, type = "response") The advantage of using predict.glm() is that you can apply the model against new data. If you want the linear predicted values on a log-odds scale, you can use: glmresult$linear.predictors or more easily: predict(glmresult) See ?fitted and ?predict.glm for more information. Let's use an example from ?infert: model1 <- glm(case ~ spontaneous+induced, data=infert,family=binomial()) # Summary of fitted values on a probability scale > summary(fitted(model1)) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1534 0.1534 0.2949 0.3347 0.3750 0.7511 # Same > summary(predict(model1, type = "response")) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1534 0.1534 0.2949 0.3347 0.3750 0.7511 # Get log-odds scale values > summary(model1$linear.predictors) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.7080 -1.7080 -0.8716 -0.7781 -0.5107 1.1050 # Same > summary(predict(model1)) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.7080 -1.7080 -0.8716 -0.7781 -0.5107 1.1050 If we wanted to do the log-odds scale to probability scale transform manually, we could do: > summary(exp(predict(model1)) / (1 + exp(predict(model1)))) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1534 0.1534 0.2949 0.3347 0.3750 0.7511 Look familiar? I would urge you to read through An Introduction To R, which is available with your R installation or via the R web site under Documentation. In addition, there are various books listed on the R web site regarding model building and related subject matter. Which you choose can be a matter of taste, but two I recommend would be: William N. Venables and Brian D. Ripley. Modern Applied Statistics with S. Fourth Edition. Springer, New York, 2002. ISBN 0-387-95457-0 Frank E. Harrell. Regression Modeling Strategies, with Applications to Linear Models, Survival Analysis and Logistic Regression. Springer, 2001. ISBN 0-387-95232-2 HTH, Marc Schwartz ______________________________________________ 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.
