That's really not what my previous post asked for (nor does it look like R at all in your photo!)
All I can suggest is you put your data in some sort of matrix structure and look at the ?cor and ?cor.test functions. Note that summary count statistics are often not enough to discern correlation structures: you need paired observations. Best, Michael On Thu, Aug 2, 2012 at 4:55 PM, danobolg321 <danobolg...@gmail.com> wrote: > Thanks for the reply. > > Maybe I am not describing this appropriately - but ultimately I am looking > for items in matrix B that co-vary with items in matrix A. > > In the case below: > http://r.789695.n4.nabble.com/file/n4638966/Slide1.jpg > > The values for Apples co-vary with Rice and Beans. > > The aim is to identify all the cases of this sort of co-variation in the > 10,000 row data set B. > > I was hoping to get a value for how "correlated" the residuals were, i.e. > Beans is more correlated than Rice, and chicken is much less correlated, in > order to generate some multiple sampling adjusted p-values later also. > > > Is this more clear? > > Thanks. > > > > -- > View this message in context: > http://r.789695.n4.nabble.com/Correlating-different-sets-of-variables-tp4638951p4638966.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > 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.
