Your example could probably be resolved with approx. If you want a more robust
solution, it looks like the fBasics package can do spline interpolation. You
may want to spline on the log of your size variable and use exp on the output
if you want to avoid negative results.
On March 5, 2021 1:14:22 AM PST, PIKAL Petr <petr.pi...@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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