Data Center
Intermountain Healthcare
[EMAIL PROTECTED]
801.408.8111
> -Original Message-
> From: Yihui Xie [mailto:[EMAIL PROTECTED]
> Sent: Sunday, October 19, 2008 6:33 AM
> To: Greg Snow
> Cc: roger koenker; r-help
> Subject: Re: [R] plot - central limit theorem
&
Thanks, Duncan, I agree with you in terms of doing the tests
independently. I've modified the code and updated the package at
R-forge.
As for the choice of vertical bars or points, you are free to provide
the option "type = 'h'" or "type = 'p'" in the function.
Regards,
Yihui
--
Yihui Xie <[EMAIL
I don't know whether showing p-values is the best approach either, but
I'm using them only as indicators to show how good the approximation
would be as the sample size increases. You may regard the p-values as
a measure of goodness of fit. I don't think I need to answer the
question of hypothesis t
111
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
project.org] On Behalf Of Yihui Xie
Sent: Wednesday, October 15, 2008 10:51 PM
To: roger koenker
Cc: r-help
Subject: Re: [R] plot - central limit theorem
Thanks, Roger, your demo is interesting. I'm thinkin
Thanks for your great help!
I have now what I wanted.
For sure it's not well written and you can realize it with much less
lines.
But it works and it's like how I wanted it to look like;
z1 <- rnorm(4500, mean=20, sd=5)
z2 <- rnorm(3600, mean=28, sd=5)
z3 <- rnorm(1300, mean=40, sd=7)
z4 <
Healthcare
[EMAIL PROTECTED]
801.408.8111
> -Original Message-
> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
> project.org] On Behalf Of Yihui Xie
> Sent: Wednesday, October 15, 2008 10:51 PM
> To: roger koenker
> Cc: r-help
> Subject: Re: [R] plot - central
Hi Jörg,
our package distrTeach has functions to visualize the central limit
theorem and the law of large numbers for "arbitrary" univariate
distributions; e.g.,
library(distrTeach)
D <- sin(Norm()) + Pois()
plot(D)
illustrateCLT(D, len = 10)
illustrateLLN(D, m = 10)
Best
Matthias
Jörg Groß
Thanks, Roger, your demo is interesting. I'm thinking about improving it later.
I've also made a demo for the CLT in my package 'animation', in which
there's also normality testing for the sample means, because I don't
think "bell-shaped" alone means normality - so I performed the
Shapiro-Wilk tes
Galton's 19th century mechanical version of this is the quincunx. I
have a
(very primitive) version of this for R at:
http://www.econ.uiuc.edu/~roger/courses/476/routines/quincunx.R
url:www.econ.uiuc.edu/~rogerRoger Koenker
email[EMAIL PROTECTED]Depart
Jörg Groß wrote:
Hi,
Is there a way to simulate a population with R and pull out m samples,
each with n values
for calculating m means?
I need that kind of data to plot a graphic, demonstrating the central
limit theorem
and I don't know how to begin.
So, perhaps someone can give me some t
Hi Joerg,
Is there a way to simulate a population with R and pull out m samples,
each with n values
for calculating m means?
I need that kind of data to plot a graphic, demonstrating the central
limit theorem
and I don't know how to begin.
So, perhaps someone can give me some tips and hints
On 10/15/2008 3:48 PM, Jörg Groß wrote:
Hi,
Is there a way to simulate a population with R and pull out m samples,
each with n values
for calculating m means?
I need that kind of data to plot a graphic, demonstrating the central
limit theorem
and I don't know how to begin.
The easiest
Healthcare
[EMAIL PROTECTED]
801.408.8111
> -Original Message-
> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
> project.org] On Behalf Of Jörg Groß
> Sent: Wednesday, October 15, 2008 1:49 PM
> To: r-help@r-project.org
> Subject: [R] plot - central limit theorem
>
&
Hi,
Is there a way to simulate a population with R and pull out m samples,
each with n values
for calculating m means?
I need that kind of data to plot a graphic, demonstrating the central
limit theorem
and I don't know how to begin.
So, perhaps someone can give me some tips and hints ho
14 matches
Mail list logo
