Thanks Abby, some info on the data: score score_SD death_count population_size x1 x1_SD y1 corr_weight 4.3 2.3 5800 900.000 5.7 6.1 250 11.000.600 .. .. .. ..
> Op 22 jun. 2020, om 02:02 heeft Abby Spurdle <spurdl...@gmail.com> het > volgende geschreven: > > I need to fix my mistakes, from earlier this morning. > The sums should be over densities, so: > > fh (X, Y) = [fh1 (X1, X1) + fh2 (X2, Y2) + ... + fhn (Xn, Yn)] / n > > fh (X, Y) = w1*fh1 (X1, X1) + w2*fh2 (X2, Y2) + ... + wn*fhn (Xn, Yn) > > assuming the weights sum to 1 > > If simulated data is used, then the expressions above can be replaced > with the union of multiple (sub)samples. > Then an estimate/inference (say correlation) can be computed from one > or more combined samples. > > Sorry, for triple posting. > > > On Mon, Jun 22, 2020 at 10:00 AM Abby Spurdle <spurdl...@gmail.com> wrote: >> >> Hi Frederick, >> >> I glanced at the webpage you've linked. >> (But only the top three snippets). >> >> This is what I would call the sum of random variables. >> (X, Y) = (X1, X1) + (X2, Y2) + ... + (Xn, Yn) >> >> The example makes the mistake of assuming that the Xs are normally >> distributed, and each of the Ys are from exactly the same uniform >> distribution. >> By "combine"-ing both approaches, are you wanting to weight each pair? >> >> w1(X1, X1) + w2(X2, Y2) + ... + wn(Xn, Yn) >> >> I note that you haven't told us much about your data. >> There may be an easier way of doing things... >> >> >> On Mon, Jun 22, 2020 at 1:53 AM Frederik Feys <fref...@gmail.com> wrote: >>> >>> Hello everyone >>> >>> At the moment I put a lot of attention in the uncertainty of my analyzes. I >>> want to do a spearman correlation that takes into account the uncertainty >>> in my observations and has weighting. >>> >>> uncertainty of observations: I came across this excellent blog that >>> proposes a bootstrap function: >>> https://www.r-bloggers.com/finding-correlations-in-data-with-uncertainty/ >>> >>> weighted: I do weighted correlations with the wCorr package. >>> >>> Now I want to combine both approaches in one approach for a final analysis. >>> How would you do that? >>> >>> Thanks for the help! >>> >>> Frederik Feys >>> PhD Medical Sciences >>> Onafhankelijk Methodoloog >>> https://www.researchgate.net/profile/Frederik_Feys >>> +32488020010 >>> >>> >>> >>> >>> >>> >>> >>> [[alternative HTML version deleted]] >>> >>> ______________________________________________ >>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >>> 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 -- To UNSUBSCRIBE and more, see 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.
