Are there missing values in your data? If so, try adding the argument na.action = na.exclude to your original call to glm or lm. It is like the default na.omit except that it records which rows were omitted (because they contained missing values) and fills in the corresponding entries in the predictions, residuals, etc. with NA's.
You can also set options(na.action = "na.exclude") to make it the default na.action in lm() and similar functions. Bill Dunlap TIBCO Software wdunlap tibco.com On Thu, Apr 23, 2015 at 10:23 AM, Mark Drummond <[email protected]> wrote: > Hi all, > > Given a simple logistic regression on a training data set using glm, > the number of predicted values is less than the number of observations > in the training set: > > > fit.train.pred <- predict(fit, type = "response") > > nrow(train) > [1] 62660 > > length(fit.train.pred) > [1] 58152 > > > > As a relative newcomer, I've run lots of simple glm, CART etc. models > but this is the first time I have seen this happen. > > Is this a common issue and is there a fix? An option to predict() perhaps? > > -- > Cheers, Mark > > Mark Drummond > [email protected] > > When I get sad, I stop being sad and be Awesome instead. TRUE STORY. > > ______________________________________________ > [email protected] 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. > [[alternative HTML version deleted]] ______________________________________________ [email protected] 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.
