Hello all: Many thanks to the people who have responded to my question, on and off-list. My problem isn't completely solved, though, and perhaps you can help again.
The problem, again, is that I have what is essentially a histogram, but not the underlying data, and I want to simulate data that would have created that histogram. That is, I have counts for the number of data points in a dozen bins. The bins are not of uniform size. (It's income data, reported as incomes from 0-10k, 10k-25k, 25k-50k, and so on.) The best suggestion I had yesterday was to simulate the data with uniform distributions in each bin, and an exponential one on the rightmost bin, and I did that and superficially it looks good. Unfortunately, now that I am trying to calibrate the model, I have discovered a high bias. The way the bins are chosen, I would expect that 9 out of 12 bins have a down-ward slope, meaning that approximating them with a square top gives me more along the high border of the bin, and I currently suspect that this is at least part of the bias. Is there a way to ask for a not-quite uniform distribution of random data? I imagine a density function with a linear, but not flat, top. I admit that the standard selection of distributions in R is more than I am familiar with, but I can't find one that does what I think I need. Any advice (R advice or statistics advice) is welcome. Thanks again, -tom -- ------------------------ tomfool at as220 dot org http://sgouros.com http://whatcheer.net ______________________________________________ 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.
