Thanks for your response Ista, I'm looking at the results in newdata following your command. I agree that predict ran, but I don't think it did what I expect it to do. I may be mistaken, but shouldn't the mean of dat$out be close to the mean of newdata$Y? Shouldn't the values in newdata$Y (assuming predict is working as expected) be similar values to:
coef(lm.res.scale)[1]+coef(lm.res.scale)[2]*newdata[,1]+coef(lm.res.scale)[3]*newdata[,2]+coef(lm.res.scale)[4]*newdata[,1]*newdata[,2] ? ----------------------------------- Russell S. Pierce, M.A. Visual Cognition Lab Department of Psychology University of California, Riverside 900 University Avenue Riverside, CA 92521 Lab Phone: (951) 827-7399 On Fri, Jan 28, 2011 at 2:31 PM, Ista Zahn <iz...@psych.rochester.edu>wrote: > Hi Russell, > There may be some subtleties that I'm not picking up on, but the > obvious problem is that the names of the predictors in newdata do not > match the names of the predictors in dat. > > names(newdata) <- names(dat)[1:2] > newdata$Y <- predict(lm.obj,newdata) > > does work on my machine. > > Best, > Ista > > On Fri, Jan 28, 2011 at 4:37 PM, Russell Pierce <rpier...@ucr.edu> wrote: > > I want to predict values from an existing lm (linear model, e.g. > > lm.obj) result in R using a new set of predictor variables (e.g. > > newdata). However, it seems that because my linear models was made by > > calling scale() on the target predictor that predict exits with an > > error, "Error in scale(xxA, center = 9.7846094491829, scale = > > 0.959413568556403) : object 'xxA' not found". By debugging predict, I > > can see that the error occurs in a call to model.frame. By debugging > > model frame I can see the error occurs with this command: variables > > <- eval(predvars, data, env); it seems likely that the error is > > because predvars looks like this: > > > > list(scale(xxA, center = 10.2058714830 <tel:+12058714830>537, scale = > 0.984627257169526), > > scale(xxB, center = 20.4491690881149, scale = 1.13765718273923)) > > > > An example case: > > > > dat <- data.frame(xxA = rnorm(20,10), xxB = rnorm(10,20)) > > dat$out <- with(dat,xxA+xxB+xxA*xxB+rnorm(20,20)) > > xVar <- "scale(xxA)" > > traceVar <- "scale(xxB)" > > DVname <- "out" > > lm.obj <- lm.res.scale <- lm(out ~ scale(xxA)*scale(xxB),data=dat) > > my.data <- lm.obj$model #load the data from the lm object > > X1 <- my.data[,xVar] > > X2 <- my.data[,traceVar] > > DV <- lm.obj$model[,DVname] > > newdata <- expand.grid(X1=c(-1,0,1),X2=c(-1,0,1)) > > newdata$X1 <- newdata$X1 * sd(my.data[,xVar]) > > newdata$X2 <- newdata$X2 * sd(my.data[,traceVar]) > > names(newdata) <- c(xVar,traceVar) #have to rename to original > > variable names for predict to work > > newdata$Y <- predict(lm.obj,newdata) > > > > Is there something I could do before passing newdata or lm.obj to > > predict() that would prevent the error? From the help file it looks > > like I might be able to do something with the terms, argument but I > > haven't quite figured out what I would need to do. Alternatively, is > > there a fix for model.frame that would prevent the error? Should > > predict() behave this way? > > > > Thanks for your time, > > > > Russell S. Pierce > > > > ______________________________________________ > > 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. > > > > > > -- > Ista Zahn > Graduate student > University of Rochester > Department of Clinical and Social Psychology > http://yourpsyche.org > [[alternative HTML version deleted]] ______________________________________________ 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.
