On Wed, May 30, 2012 at 12:58 AM, David Winsemius <dwinsem...@comcast.net> wrote: > > On May 29, 2012, at 6:32 PM, jacaranda tree wrote: > >> Hi all, >> I have a data set (df, n=10 for the sake of simplicity here) where I have >> two continuous variables (age and weight) and I also have a grouping >> variable (group, with two levels). I want to run correlations for each group >> separately (kind of similar to "split file" in SPSS). I've been >> experimenting with different functions, and I was able to do this correctly >> using ddply function, but output is a little bit difficult to read when I do >> the cor.test to get all the data with p values, df, and pearson r (see >> below). I also tried to do it with by function. Although, with by, it shows >> the data for two groups separately, it seems like it calculates the same r >> for both groups. Here is my code for both ddply and by, and the output as >> well. I was wondering if there is a way to display the output better with >> ddply or run the correlations correctly for each group using by. >> Thanks in advance, >> > > I would have imagined something along the lines of > > lapply( split( df, df$group, function(x) cor.test(x[["age"]], x[["weight")] > )
lapply( split( df, df$group), function(x) cor.test(x[["age"]], x[["weight"]]) ) I'd imagine (I've been hunting down missing parentheses all night so excuse the pedantry) Repeating David's disclaimer "... but without an example it's only a guess." Best, M > > ... but without an example it's only a guess. > > -- > David > >> 1.with "ddply" >> r<-ddply(df, .(group), summarise, "corr" = cor.test(age, weight, method = >> "pearson")) >> >> Output: >> Group corr >> 1 1 Inf >> 2 1 3 >> 3 1 0 >> 4 1 1 >> 5 1 0 >> 6 1 two.sided >> 7 1 Pearson's product-moment correlation >> 8 1 age and weight >> 9 1 1, 1 >> 10 2 9.722211 >> 11 2 3 >> 12 2 0.002311412 >> 13 2 0.9844986 >> 14 2 0 >> 15 2 two.sided >> 16 2 Pearson's product-moment correlation >> 17 2 age and weight >> 18 2 0.7779640, 0.9990233 >> >> 2. with "by" >> r <- by(df, group, FUN = function(x) cor.test(age, weight, method = >> "pearson")) >> >> Output: >> Group: 1 >> >> Pearson's product-moment correlation >> >> data: age and weight >> t = 6.4475, df = 8, p-value = 0.0001988 >> alternative hypothesis: true correlation is not equal to 0 >> 95 percent confidence interval: >> 0.6757758 0.9802100 >> sample estimates: >> cor >> 0.9157592 >> >> ------------------------------------------------------------ >> Group: 2 >> >> Pearson's product-moment correlation >> >> data: age and weight >> t = 6.4475, df = 8, p-value = 0.0001988 >> alternative hypothesis: true correlation is not equal to 0 >> 95 percent confidence interval: >> 0.6757758 0.9802100 >> sample estimates: >> cor >> 0.9157592 >> [[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. > > > ______________________________________________ > 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.
