quantile from quantile table calculation without original data
I'm sorry. I misread your example, this morning. (I didn't read the code after the line that calls plot). After looking at this problem again, interpolation doesn't apply, and extrapolation would be a last resort. If you can assume your data comes from a particular type of distribution, such as a lognormal distribution, then a better approach would be to find the most likely parameters. i.e. This falls within the broader scope of maximum likelihood. (Except that you're dealing with a table of quantile-probability pairs, rather than raw observational data). I suspect that there's a relatively easy way of finding the parameters. I'll think about it... But someone else may come back with an answer first...
On Sat, Mar 6, 2021 at 8:17 AM Abby Spurdle <spurdle.a at gmail.com> wrote:
I note three problems with your data: (1) The name "percent" is misleading, perhaps you want "probability"? (2) There are straight (or near-straight) regions, each of which, is equally (or near-equally) spaced, which is not what I would expect in problems involving "quantiles". (3) Your plot (approximating the distribution function) is back-the-front (as per what is customary). On Fri, Mar 5, 2021 at 10:14 PM PIKAL Petr <petr.pikal at precheza.cz> wrote:
Dear all
I have table of quantiles, probably from lognormal distribution
dput(temp)
temp <- structure(list(size = c(1.6, 0.9466, 0.8062, 0.6477, 0.5069,
0.3781, 0.3047, 0.2681, 0.1907), percent = c(0.01, 0.05, 0.1,
0.25, 0.5, 0.75, 0.9, 0.95, 0.99)), .Names = c("size", "percent"
), row.names = c(NA, -9L), class = "data.frame")
and I need to calculate quantile for size 0.1
plot(temp$size, temp$percent, pch=19, xlim=c(0,2))
ss <- approxfun(temp$size, temp$percent)
points((0:100)/50, ss((0:100)/50))
abline(v=.1)
If I had original data it would be quite easy with ecdf/quantile function but without it I am lost what function I could use for such task.
Please, give me some hint where to look.
Best regards
Petr
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