Re: [R] Empirical density estimation

Jeff Newmiller Sun, 11 Mar 2018 20:21:00 -0700

cwhmisc package provides essentially the algorithm outlined by Dan.

If you want answers outside your original data (extrapolation) then thefollowing code at least won't give broken answers, though it is notnecessarily any more "correct" for extrapolation than the approx solutionis.

Regarding "needing this for reporting", do thoroughly read ?density asBert suggested, because the bandwidth parameter affects your answers andthere are various historical recommendations for choosing possiblebandwidth values, and really no "right" answer.


############
smoothed.df2 <- function ( d ) {
    F <- cumsum( d$y )
    F <- F / F[ length( F ) ] * ( length( F ) - 0.5 ) / length( F )
    eF <- splinefun( d$x, qlogis( F ), "monoH.FC" )
    function( x ) {
        efx <- eF( x )
        plogis( efx )
    }
}

set.seed( 42 )
Dat <- c( rnorm( 100, 1 ), rnorm( 100, 5 ) )

d <- density( Dat )

CDF1 <- cwhmisc::smoothed.df( d )
plot( Dat, CDF1( Dat ) )

CDF2 <- smoothed.df2( d )
plot( Dat, CDF2( Dat ) )

CDF1( -5 ) # <0
CDF2( -5 ) # >0
############

On Sun, 11 Mar 2018, Daniel Nordlund wrote:

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 the kernel density estimate
dens <- density(Dat)

## estimate the density at your original points
dnew <- approx(dens$x,dens$y,xout=Dat)

## plot kernel density estimate
plot(dx)

## add your original values with the estimated density
points(dnew, pch=1, cex=0.5, col="red")


Hope this is helpful,

Dan

--
Daniel Nordlund
Port Townsend, WA  USA

On Mon, Mar 12, 2018 at 3:49 AM, Bert Gunter <bgunter.4...@gmail.com>wrote:

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 keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

On Sun, Mar 11, 2018 at 11:45 AM, Christofer Bogaso
<bogaso.christo...@gmail.com> wrote:


Hi,

Let say I have below vector of data-points :

Dat = c(-0.444444444444444, -0.25, -0.237449799196787,-0.227467046669042,


-0.227454464682363, -0.22, -0.214876033057851, -0.211781206171108,

-0.199891067538126, -0.192920353982301, -0.192307692307692,
-0.186046511627907,

-0.184418145956608, -0.181818181818182, -0.181818181818182,
-0.181266261925412,

-0.181003118503119, -0.179064587973274, -0.178217821782178,
-0.17809021675454,

-0.177685950413223, -0.177570093457944, -0.176470588235294,
-0.176470588235294,

-0.174825741611282, -0.168021680216802, -0.166666666666667,
-0.166666666666667,

-0.166380789022298, -0.164209115281501, -0.164011246485473,
-0.162689804772234,

-0.162361623616236, -0.160161507402423, -0.16, -0.155038759689922,

-0.154172560113154, -0.15311004784689, -0.151515151515152,
-0.151462994836489,

-0.151098901098901, -0.150537634408602, -0.150442477876106,
-0.150406504065041,

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-0.144021739130435,

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0.117117117117117,

0.123532699832309, 0.141304347826087, 0.179487179487179,
0.191268191268191,

0.205128205128205, 0.219020172910663, 0.271590909090909,0.333333333333333


)


Now I want to estimate empirical PDF for interval of (-1, 1) including
all points of Dat

I have looked into the density() function, however it appears that,
empirical density is estimated for equally distant points, which not
necessarily contains actual supplied points (in my case, Dat)

Is there any option to achieve the same?

Thanks for your pointer.

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and provide commented, minimal, self-contained, reproducible code.


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Re: [R] Empirical density estimation

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