cwhmisc package provides essentially the algorithm outlined by Dan.
If you want answers outside your original data (extrapolation) then the
following code at least won't give broken answers, though it is not
necessarily any more "correct" for extrapolation than the approx solution
is.
Regard
On 3/11/2018 3:35 PM, Christofer Bogaso wrote:
But for my reporting purpose, I need to generate a bell curve like
plot based on empirical PDF, that also contains original points.
Any idea would be helpful. Thanks,
Christofer,
something like the following may get you what you want:
## get t
But for my reporting purpose, I need to generate a bell curve like
plot based on empirical PDF, that also contains original points.
Any idea would be helpful. Thanks,
On Mon, Mar 12, 2018 at 3:49 AM, Bert Gunter wrote:
> You need to re-read ?density and perhaps think again -- or do some study --
Hi Christofer,
You may be looking for ecdf (stats) for a start, then working out a
way to translate the cumulative density values into probability
values.
Jim
On Mon, Mar 12, 2018 at 5:45 AM, Christofer Bogaso
wrote:
> Hi,
>
> Let say I have below vector of data-points :
>
> Dat = c(-0.
You need to re-read ?density and perhaps think again -- or do some study --
about how a (kernel) density estimate works. The points at which the
estimate is calculated are *not* the values given, nor should they be!
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people
Hi,
Let say I have below vector of data-points :
Dat = c(-0.444, -0.25, -0.237449799196787, -0.227467046669042,
-0.227454464682363, -0.22, -0.214876033057851, -0.211781206171108,
-0.199891067538126, -0.192920353982301, -0.192307692307692, -0.186046511627907,
-0.184418145956608, -0.
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