Folks, Normally, in a data frame, one observation counts as one observation of the distribution. Thus one can easily produce a CDF and (in Splus atleast) use cdf.compare to compare the CDF (BTW: what is the R equivalent of the SPlus cdf.compare() function, if any?)
However, if each point should not count equally, how can I weight the points before comparing the distributions? I was thinking of somehow creating multiple observations for each actual observation based on weights and creating a new dataframe etc. -- but that seem excessive. Surely there is a simpler way? > x <- rnorm(100) > y <- rnorm(10) > xw <- rnorm(100) * 1.73 # The weights. These won't add up to 1 or N or > anything because of missing values. > yw <- rnorm(10) * 6.23 # The weights. These won't add up to 1 or to the same > number as xw. > # The question to answer is, how can I create a qq plot or cdf compare of x > vs. y, weighted by their weights, xw and yw (to eventually figure out if y > comes from the population x, similar to Kolmogorov-Smirnov GOF)? > qqplot(x,y) # What now? Thanks for any help, -- -- Vivek Satsangi Student, Rochester, NY USA Life is short, the art long, opportunity fleeting, experiment treacherous, judgement difficult. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
