One (awkward) way to do this is: x <- matrix(c(c(test),c(test2)),ncol=2) y <- rowMeans(x,na.rm=TRUE) testave <- matrix(y,nrow=nrow(test))
--- On Tue, 14/7/09, Tish Robertson <tishrobert...@hotmail.com> wrote: > From: Tish Robertson <tishrobert...@hotmail.com> > Subject: [R] averaging two matrices whilst ignoring missing values > To: r-help@r-project.org > Received: Tuesday, 14 July, 2009, 11:46 AM > > Hi folks, > > > > I'm trying to do something that seems like it should easy, > but it apparently isn't. I have two large matrices, > both containing a number of missing values at different > cells. I would like to average these matrices, but the NAs > are preventing me. I get a "non-numeric argument to binary > operator" error. That's the first problem. > > > > test<-read.csv("test.csv",header=FALSE) > test2<-read.csv("test2.csv",header=FALSE) > test_ <- as.matrix(test,na.rm=T) > test2_ <- as.matrix(test2,na.rm=T) > testave<- (test_+test2_)/2 > > ?? > > > > So off the bat I'm doing something wrong. > > > > How would I replace the missing values in one matrix with > the corresponding non-missing values in another? It's > acceptable to me if I only have one value representing the > average for a particular coordinate. > > > > Any help would be appreciated! > > > > > > > > _________________________________________________________________ > Bing™ finds low fares by predicting when to book. Try it > now. > > =WLHMTAG&crea=TXT_MTRHPG_Travel_Travel_TravelDeals_1x1 > [[alternative HTML version deleted]] > > > -----Inline Attachment Follows----- > > ______________________________________________ > 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. > ______________________________________________ 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.