From: Ted Harding <Ted.Harding_at_manchester.ac.uk>

Date: Thu, 27 Mar 2008 14:00:48 +0000 (GMT)

13:15 0.956 0.132 | 1.39 0.282

E-Mail: (Ted Harding) <Ted.Harding_at_manchester.ac.uk> Fax-to-email: +44 (0)870 094 0861

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. Received on Thu 27 Mar 2008 - 18:10:05 GMT

Date: Thu, 27 Mar 2008 14:00:48 +0000 (GMT)

Your variables dat$x and dat$y have:

mean(dat$x[1:12])

## [1] 0.1519167

mean(dat$y[1:12])

## [1] 0.1807932

Note that, for the first 12 in each case, the SD less that 1/5 of the mean:

mean(dat$x[1:12])/sd(dat$x[1:12])

## [1] 6.207186

mean(dat$y[1:12])/sd(dat$y[1:12])

## [1] 5.348779

dat$x dat$y Mean SD Mean SD ------------------------+------------------- 1:12 0.152 0.024 | 0.181 0.034 |

13:15 0.956 0.132 | 1.39 0.282

Note that for dat$x Mean/SD approx = 6 for each sub-series, and for data$y Mean/SD approx = 5 for each subseries, so you could be looking at results which display a nearly constant coefficient of variation. Now, this is indeed a property of the log-normal distribution (as well as of others), so that could indeed be worth considering. However, you still have to account for the apparent split noted above into distinct groups.

So you are really facing a modelling question: why did the numbers come out as they did, and what is a good way to represent that mechanism as a distribution?

With best wishes,

Ted.

On 27-Mar-08 12:27:55, Tom Cohen wrote:

*>
*

> Dear list,

*> I have a dataset containing values obtained from two different
**> instruments (x and y).
**> I want to generate 5 samples from normal distribution for each
**> instrument based on
**> their means and standard deviations. The problem is values from both
**> instruments are
**> non-negative, so if using rnorm I would get some negative values. Is
**> there any options
**> to determine the lower bound of normal distribution to be 0 or can I
**> simulate the
**> samples in different ways to avoid the negative values?
**>
**>
**> > dat
**> id x y
**> 75 101 0.134 0.1911315
**> 79 102 0.170 0.1610306
**> 76 103 0.134 0.1911315
**> 84 104 0.170 0.1610306
**> 74 105 0.134 0.1911315
**> 80 106 0.170 0.1610306
**> 77 107 0.134 0.1911315
**> 81 108 0.170 0.1610306
**> 82 109 0.170 0.1610306
**> 78 111 0.170 0.1610306
**> 83 112 0.170 0.1610306
**> 85 113 0.097 0.2777778
**> 2 201 1.032 1.5510434
**> 1 202 0.803 1.0631001
**> 5 203 1.032 1.5510434
**>
**> mu<-apply(dat[,-1],2,mean)
**> sigma<-apply(dat[,-1],2,sd)
**> len<-5
**> n<-20
**> s1<-vector("list",len)
**> set.seed(7)
**> for(i in 1:len){
**> s1[[i]]<-cbind.data.frame(x=rnorm(n*i,mean=mu[1],sd=sigma[1]),
**> y=rnorm(n*i,mean=mu[2],sd=sigma[2]))
**> }
**>
**> Thanks for any help,
**> Tom
**>
**>
**> ---------------------------------
**> Sök efter kärleken!
**>
**> [[alternative HTML version deleted]]
**>
*

E-Mail: (Ted Harding) <Ted.Harding_at_manchester.ac.uk> Fax-to-email: +44 (0)870 094 0861

Date: 27-Mar-08 Time: 14:00:44 ------------------------------ XFMail ------------------------------ ______________________________________________R-help_at_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. Received on Thu 27 Mar 2008 - 18:10:05 GMT

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