I predicted that y would increase as x increased. However, I only made the prediction on the ranks of the scores. The ranks don't correlate with predicted. And, I don't think a regression on the ranks is warranted. However, the actual scores do yield a significant slope for b, and a significant R^2 using a linear regression (y is the value and x is the predicted rank). What should my argument be here? Should I have endorsed using the actual scores instead of ranks to begin for some reason that doesn't have anything to do with my current result? :)
OK, now I realize that I should probably not have been correlating ranks in the first place because my real data may have had a non-linear, but still steadily increasing, slope. The ranks would tend to increase variance where the slope was low and ruined my chance of finding an effect.
Oh, on another note, I can use rcorr to get the Spearman correlations, but I'd like to be able to just add
the ranks as a column. I was going to just use order and add a simple factor. But, that doesn't deal with ties correctly.
still don't have these yet.
And, I also wanted to analyze correlations subject by subject and compare my two groups. However, there doesn't seem to be a good way to get this. I tried using "by" with "cor". However, this requires binding x and y which causes cor to return a matrix (if you could pass it x and y separate it would just return a number).
given
data frame s x y subj 4 7 harry 5 1 harry 6 9 harry 2 4 steve 3 7 steve ...
i'd like to be able to produce
r subj .12 harry .52 steve ...
any tips?
______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
