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, F
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:
http://r.789695.n4.nabble.com/how-to-gene
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.
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:
http://r.789695.n4.nabble.com/how-to-gener
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:
http://r.789695.n4.nabble.com/how-to-generate-
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:
http://r.789695.n4.nabble.com/how-to
Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.s...@imail.org
801.408.8111
> -Original Message-
> From: Frank Harrell [mailto:f.harr...@vanderbilt.edu]
> Sent: Tuesday, July 27, 2010 4:54 PM
> To: Greg Snow
> Cc: xin wei; r-help@r-project.org
> Subject: Re: [R
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
On
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 Message-
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 wei
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>
Content-Type: text/pla
> -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 s
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/
http://cran.r-project.org/web/packages/distrDoc/vignettes/distr.pd
Hi:
On Mon, Jul 26, 2010 at 11:36 AM, 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 d
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 (
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
w
15 matches
Mail list logo
