d Winsemius
> > Sent: Sunday, March 7, 2021 1:33 AM
> > To: Abby Spurdle ; PIKAL Petr
> >
> > Cc: r-help@r-project.org
> > Subject: Re: [R] quantile from quantile table calculation without original
> > data
> >
> >
> > On 3/6/21 1:02 AM, Abby S
David Winsemius
> Sent: Sunday, March 7, 2021 1:33 AM
> To: Abby Spurdle ; PIKAL Petr
>
> Cc: r-help@r-project.org
> Subject: Re: [R] quantile from quantile table calculation without original
> data
>
>
> On 3/6/21 1:02 AM, Abby Spurdle wrote:
> > I came up w
I am aware of that... I have my own functions for this purpose that use
splinefun. But if you are trying to also do other aspects of probability
distribution calculations, it looked like using fBasics would be easier than
re-inventing the wheel. I could be wrong, though, since I haven't used
> Jeff Newmiller
> on Fri, 05 Mar 2021 10:09:41 -0800 writes:
> 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.
base R's spline package does spline interpolation
On 3/6/21 1:02 AM, Abby Spurdle wrote:
I came up with a solution.
But not necessarily the best solution.
I used a spline to approximate the quantile function.
Then use that to generate a large sample.
(I don't see any need for the sample to be random, as such).
Then compute the sample mean
I came up with a solution.
But not necessarily the best solution.
I used a spline to approximate the quantile function.
Then use that to generate a large sample.
(I don't see any need for the sample to be random, as such).
Then compute the sample mean and sd, on a log scale.
Finally, plug
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
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
On 3/5/21 1:14 AM, PIKAL Petr 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)),
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
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 =
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