On Tue, 15 Jan 2008, Roland Rau wrote: > Hi, > > maybe I missed something while using SAS or SPSS. So please make sure > that I am not talking nonsense here. > > - How would you re-use results in SPSS or SAS? If it is possible for SAS > and SPSS, I am fairly sure it is not as easy as in R: > lmmodel1 <- lm(Y~X) > myslope <- coef(lmmodel1)[2]
taking off on the 're-use' idea here is a simple, instructive graphic: iris.cluster <- hclust( dist( iris[,-5]) ) plot( iris[,-5], col=cutree( iris.cluster, k=4)) and here we can see if the clustering and choice of 4 clusters was informative: table( iris[,5], cutree( iris.cluster, k=4 )) Can SAS/SPSS do this easily? One of the things that makes R/S nice is the existence of sensible methods for plot, summary, and so on. Chuck > - You have population and death data on the individual level classified > by year, age, sex, and country. Now you want to calculate the > probability of dying by year, age, sex, and country. > In R, i would do: > pop.array <- tapply(X=popdata$Count, > INDEX=list(Age=popdata$Age, > Year=popdata$Year, > Sex=popdata$Sex, > Country=popdata$Country), > FUN=sum) > dth.array <- tapply(X=dthdata$Count, > INDEX=list(Age=dthdata$Age, > Year=dthdata$Year, > Sex=dthdata$Sex, > Country=dthdata$Country), > FUN=sum) > prop.dying.array <- dth.array / pop.array > > Now you can easily extract a vector of the probability of dying of 85 > year-old men dying in the first year of observation in all countries by > writing: > prop.dying.array[86,1,1,] > - I hope I am wrong on this one. But when I was using SPSS, I could not > find any possibility to include left truncated data in survival > analysis. Maybe I did not find this possibility or maybe it has been > included since. > - The function outer() > - Data are not always rectangular data frames. > > > Those are just a few thoughts which came to my mind. > I hope this helps, > Roland > > > > Matthew Keller wrote: >> Hi all, >> >> I'm giving a talk in a few days to a group of psychology faculty and >> grad students re the R statistical language. Most people in my dept. >> use SAS or SPSS. It occurred to me that it would be nice to have a few >> concrete examples of things that are fairly straightforward to do in R >> but that are difficult or impossible to do in SAS or SPSS. However, it >> has been so long since I have used either of those commercial products >> that I am drawing a blank. I've searched the forums and web for a list >> and came up with just Bob Muenchen's comparison of general procedures >> and Patrick Burns' overview of the three. Neither of these give >> concrete examples of statistical problems that are easily solved in R >> but not the commercial packages. >> >> Can anyone more familiar with SAS or SPSS think of some examples of >> problems that they couldn't do in one of those packages but that could >> be done easily in R? Similarly, if there are any examples of the >> converse I would also be interested to know. >> >> Best, >> >> Matt >> > > ______________________________________________ > 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. > Charles C. Berry (858) 534-2098 Dept of Family/Preventive Medicine E mailto:[EMAIL PROTECTED] UC San Diego http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901 ______________________________________________ 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.
