hi, Frank:
how can we make sure the randomly sampled data follow the same distribution
as the original dataset? i assume each data point has the same prabability
to be selected in a simple random sampling scheme.
thanks
--
View this message in context:
This is true by definition.
Read about the bootstrap which may give you some good background
information.
Frank E Harrell Jr Professor and ChairmanSchool of Medicine
Department of Biostatistics Vanderbilt University
On Wed, 28 Jul 2010, xin wei wrote:
hi,
David Winsemius wrote:
On Jul 26, 2010, at 2:36 PM, xin wei wrote:
hi, this is more a statistical question than a R question. but I do
want to
know how to implement this in R.
I have 10,000 data points. Is there any way to generate a empirical
probablity distribution from it (the problem
Hi:
On Mon, Jul 26, 2010 at 11:36 AM, xin wei xin...@stat.psu.edu wrote:
hi, this is more a statistical question than a R question. but I do want to
know how to implement this in R.
I have 10,000 data points. Is there any way to generate a empirical
probablity distribution from it (the
Hi Dennis,
you should take a look at the CRAN task view for distributions
http://cran.r-project.org/web/views/Distributions.html
Beside that our distr-family of packages might be useful, see also
http://www.jstatsoft.org/v35/i10/
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
project.org] On Behalf Of xin wei
Sent: Monday, July 26, 2010 11:36 AM
To: r-help@r-project.org
Subject: [R] how to generate a random data from a empirical
distribition
hi, this is more a
On 7/27/2010 6:00 AM, r-help-requ...@r-project.org wrote:
Date: Mon, 26 Jul 2010 11:36:29 -0700 (PDT)
From: xin weixin...@stat.psu.edu
To:r-help@r-project.org
Subject: [R] how to generate a random data from a empirical
distribition
Message-ID:1280169389379-2302716.p...@n4.nabble.com
Another option for fitting a smooth distribution to data (and generating future
observations from the smooth distribution) is to use the logspline package.
--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.s...@imail.org
801.408.8111
-Original
Easiest thing is to sample with replacement from the original data.
This is the idea behind the bootstrap, which is sampling from the
empirical CDF.
Frank E Harrell Jr Professor and ChairmanSchool of Medicine
Department of Biostatistics Vanderbilt University
If they want to generate directly from the empirical distribution, then
sampling with replacement is the best choice (others had already suggested
that). But the reference in the original post to the normal and beta
distributions suggested to me that the original poster may have wanted a
Dennis:
points well taken. It seems to be important to investigate the nature of
distribution. I might be too naive to assume a emiprical probability
distribution will be simply calculated from a clound of data points...
--
View this message in context:
hi, Dennis:
points well taken. it seems to be important to investigate the nature of
distribution. I may be too naive to assume a empirical probability
distribution would be computed from a could of data points
--
View this message in context:
good point. It seems to be important to investigate the nature of
distribution. I might be too naive to assume that a empirical probability
distribution would be automatically generated from a cloud of data
points.
--
View this message in context:
this is very insightful. sounds exactly like what I want to do.
thanks. Frank.
--
View this message in context:
http://r.789695.n4.nabble.com/how-to-generate-a-random-data-from-a-empirical-distribition-tp2302716p2304346.html
Sent from the R help mailing list archive at Nabble.com.
hi, this is more a statistical question than a R question. but I do want to
know how to implement this in R.
I have 10,000 data points. Is there any way to generate a empirical
probablity distribution from it (the problem is that I do not know what
exactly this distribution follows, normal,
On Jul 26, 2010, at 2:36 PM, xin wei wrote:
hi, this is more a statistical question than a R question. but I do
want to
know how to implement this in R.
I have 10,000 data points. Is there any way to generate a empirical
probablity distribution from it (the problem is that I do not know
16 matches
Mail list logo
