Re: [R] Histogram to KDE
To bootstrap from a histogram, use
sample(bins, replace = TRUE, prob = counts)
Note that a kernel density estimate is biased, so some bootstrap
confidence intervals have poor coverage properties.
Furthermore, if the kernel bandwidth is data-driven then the estimate
is not functional, so some bootstrap and jackknife methods won't work right.
Tim Hesterberg
http://www.timhesterberg.net
New: Mathematical Statistics with Resampling and R, Chihara Hesterberg
On Fri, Aug 31, 2012 at 12:15 PM, David L Carlson dcarl...@tamu.edu wrote:
Using a data.frame x with columns bins and counts:
x - structure(list(bins = c(3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5,
11.5, 12.5, 13.5, 14.5, 15.5), counts = c(1, 1, 2, 3, 6, 18,
19, 23, 8, 10, 6, 2, 1)), .Names = c(bins, counts), row.names =
4:16,
class = data.frame)
This will give you a plot of the kde estimate:
Thanks.
xkde - density(rep(bins, counts), bw=SJ)
plot(xkde)
As for the standard error or the confidence interval, you would probably
need to use bootstrapping.
On a similar note - is there a way in R to directly resample /
cross-validate from a histogram of a data-set without recreating the
original data-set ?
-Original Message-
Hello,
I wanted to know if there was way to convert a histogram of a data-set
to a
kernel density estimate directly in R ?
Specifically, I have a histogram [bins, counts] of samples {X1 ...
XN} of a quantized variable X where there is one bin for each level of
X,
and I'ld like to directly get a kde estimate of the pdf of X from the
histogram. Therefore, there is no additional quantization of X in the
histogram. Most KDE methods in R seem to require the original sample
set - and I would like to avoid re-creating the samples from the
histogram. Is there some quick way of doing this using one of the
standard
kde methods in R ?
Also, a general statistical question - is there some measure of the
standard error or confidence interval or similar of a KDE of a data-set
?
Thanks,
-fj
[[alternative HTML version deleted]]
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Re: [R] Histogram to KDE
On Fri, Aug 31, 2012 at 12:15 PM, David L Carlson dcarl...@tamu.edu wrote:
Using a data.frame x with columns bins and counts:
x - structure(list(bins = c(3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5,
11.5, 12.5, 13.5, 14.5, 15.5), counts = c(1, 1, 2, 3, 6, 18,
19, 23, 8, 10, 6, 2, 1)), .Names = c(bins, counts), row.names =
4:16,
class = data.frame)
This will give you a plot of the kde estimate:
Thanks.
xkde - density(rep(bins, counts), bw=SJ)
plot(xkde)
As for the standard error or the confidence interval, you would probably
need to use bootstrapping.
On a similar note - is there a way in R to directly resample /
cross-validate from a histogram of a data-set without recreating the
original data-set ?
-Original Message-
Hello,
I wanted to know if there was way to convert a histogram of a data-set
to a
kernel density estimate directly in R ?
Specifically, I have a histogram [bins, counts] of samples {X1 ...
XN} of a quantized variable X where there is one bin for each level of
X,
and I'ld like to directly get a kde estimate of the pdf of X from the
histogram. Therefore, there is no additional quantization of X in the
histogram. Most KDE methods in R seem to require the original sample
set - and I would like to avoid re-creating the samples from the
histogram. Is there some quick way of doing this using one of the
standard
kde methods in R ?
Also, a general statistical question - is there some measure of the
standard error or confidence interval or similar of a KDE of a data-set
?
Thanks,
-fj
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] Histogram to KDE
Hello,
I wanted to know if there was way to convert a histogram of a data-set to a
kernel density estimate directly in R ?
Specifically, I have a histogram [bins, counts] of samples {X1 ...
XN} of a quantized variable X where there is one bin for each level of X,
and I'ld like to directly get a kde estimate of the pdf of X from the
histogram. Therefore, there is no additional quantization of X in the
histogram. Most KDE methods in R seem to require the original sample
set - and I would like to avoid re-creating the samples from the
histogram. Is there some quick way of doing this using one of the standard
kde methods in R ?
Also, a general statistical question - is there some measure of the
standard error or confidence interval or similar of a KDE of a data-set ?
Thanks,
-fj
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Histogram to KDE
Using a data.frame x with columns bins and counts:
x - structure(list(bins = c(3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5,
11.5, 12.5, 13.5, 14.5, 15.5), counts = c(1, 1, 2, 3, 6, 18,
19, 23, 8, 10, 6, 2, 1)), .Names = c(bins, counts), row.names =
4:16,
class = data.frame)
This will give you a plot of the kde estimate:
xkde - density(rep(bins, counts), bw=SJ)
plot(xkde)
As for the standard error or the confidence interval, you would probably
need to use bootstrapping.
--
David L Carlson
Associate Professor of Anthropology
Texas AM University
College Station, TX 77843-4352
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
project.org] On Behalf Of firdaus.janoos
Sent: Friday, August 31, 2012 9:52 AM
To: r-help@r-project.org
Subject: [R] Histogram to KDE
Hello,
I wanted to know if there was way to convert a histogram of a data-set
to a
kernel density estimate directly in R ?
Specifically, I have a histogram [bins, counts] of samples {X1 ...
XN} of a quantized variable X where there is one bin for each level of
X,
and I'ld like to directly get a kde estimate of the pdf of X from the
histogram. Therefore, there is no additional quantization of X in the
histogram. Most KDE methods in R seem to require the original sample
set - and I would like to avoid re-creating the samples from the
histogram. Is there some quick way of doing this using one of the
standard
kde methods in R ?
Also, a general statistical question - is there some measure of the
standard error or confidence interval or similar of a KDE of a data-set
?
Thanks,
-fj
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-
guide.html
and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
