2 Jun 2010 23:39

### Online Interactive Statistics Courses for July and August

```Interact with our experts. Participate from office or home at your
http://statcourse.com.

Analyzing Large Numbers of Variablesâ€”Microarrays, EEG's, MRI's.  This
4-week course will show you how to analyze data with large numbers of
variables most effectively and introduce you to the software that can do
the job.  Emphasis on the use of the bootstrap, decision trees,
multivariate regression, and permutation tests.  July 9th 2010 to Aug
6th 2010. \$404 (includes textbook).   Discount of \$50 for students,
faculty, and research workers at academic and research institutions and
government employees.  Write courses <at> statcourse.com to obtain discounts.

Sample Size Determination  Three lessons in three weeks.  July 9th to
July 30th 2010.  Cost \$299.  Early Bird discount of \$25 before July 1st
2010.  Students, faculty and research workers at academic institutions
are eligible for a further \$50 discount.   Write courses <at> statcourse.com
to obtain discounts.

Manager's Guide to Design and Conduct of Clinical Trials.  Four lessons
in four weeks. July 30th 2010 to August 27th 2010. \$495 per participant.
(\$375 for each additional person at the same firm, institution or
government office.  \$375 for those with an academic or institutional
email address.)  Plus further early bird discount of \$50 if you register
http://statcourse.com/ctrials.htm.

Modeling with R.  Three lessons in three weeks. July 9th to 31st 2010.
```

4 Jun 2010 07:22

### quasi-monte carlo integration for mixed discrete and continuous variables

Hi, all,

Are there any low-discrepancy sequences to do quasi-monte carlo integration for a multivariate distribution with both discrete and continuous variables?
For example, the continuous variable (A) follows a normal distribution and the discrete variable (B) is binary. The mean of  the normal distribution  depends on the value of B.

Can S-plus generate such sequences?

Thanks.

4 Jun 2010 11:14

### Re: quasi-monte carlo integration for mixed discrete and continuous variables

```Quasirandom sequences typically fall in [0,1]. You can model either
continuous or discrete variables by scaling (continuous) or
decision-point discretization (discrete). E.g., for a binary
variable, code "0" for the quasirandom number < 0.5 and "1" if greater.

I would suggest Halton or Hammersley sequences as an initial method
to consider.

At 01:22 AM 6/4/2010, Wei Ye wrote:
>Hi, all,
>
>Are there any low-discrepancy sequences to do quasi-monte carlo
>integration for a multivariate distribution with both discrete and
>continuous variables?
>For example, the continuous variable (A) follows a normal
>distribution and the discrete variable (B) is binary. The mean
>of  the normal distribution  depends on the value of B.
>
>Can S-plus generate such sequences?
>
>Thanks.
>
>
>

================================================================
Robert A. LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral <at> lcfltd.com
Least Cost Formulations, Ltd.            URL: http://lcfltd.com/
824 Timberlake Drive                     Tel: 757-467-0954
Virginia Beach, VA 23464-3239            Fax: 757-467-2947

"Vere scire est per causas scire"
================================================================

--------------------------------------------------------------------
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```
4 Jun 2010 17:37

### Re: quasi-monte carlo integration for mixed discrete and continuous variables

```Thank you.

It is  not a problem to generate the points for either a binary variable or a continuous variable. My question
is how to generate the points for both a binary variable and a continuous variable with the latter
distribution depending on the value of the former.

----- Original Message ----
From: Robert A LaBudde <ral <at> lcfltd.com>
To: Wei Ye <weiye1 <at> yahoo.com>
Cc: s-news <at> wubios.wustl.edu
Sent: Fri, June 4, 2010 2:14:05 AM
Subject: Re: [S] quasi-monte carlo integration for mixed discrete and continuous variables

Quasirandom sequences typically fall in [0,1]. You can model either continuous or discrete variables by
scaling (continuous) or decision-point discretization (discrete). E.g., for a binary variable, code
"0" for the quasirandom number < 0.5 and "1" if greater.

I would suggest Halton or Hammersley sequences as an initial method to consider.

At 01:22 AM 6/4/2010, Wei Ye wrote:
> Hi, all,
>
> Are there any low-discrepancy sequences to do quasi-monte carlo integration for a multivariate
distribution with both discrete and continuous variables?
> For example, the continuous variable (A) follows a normal distribution and the discrete variable (B) is
binary. The mean of  the normal distribution  depends on the value of B.
>
> Can S-plus generate such sequences?
>
> Thanks.
>
>
>

================================================================
Robert A. LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral <at> lcfltd.com
Least Cost Formulations, Ltd.            URL: http://lcfltd.com/
824 Timberlake Drive                     Tel: 757-467-0954
Virginia Beach, VA 23464-3239            Fax: 757-467-2947

"Vere scire est per causas scire"
================================================================

--------------------------------------------------------------------
This message was distributed by s-news <at> lists.biostat.wustl.edu.  To
unsubscribe send e-mail to s-news-request <at> lists.biostat.wustl.edu with
the BODY of the message:  unsubscribe s-news

```
4 Jun 2010 20:05

### Re: quasi-monte carlo integration for mixed discrete and continuous variables

```Thanks. But I am asking how to generate quasi monte carlo points, not pseudo random points.

Quasi monte carlo points are deterministic.

----- Original Message ----
From: "Benoist, Christophe O." <Christophe_Benoist <at> hms.harvard.edu>
To: Wei Ye <weiye1 <at> yahoo.com>; Robert A LaBudde <ral <at> lcfltd.com>
Cc: "s-news <at> wubios.wustl.edu" <s-news <at> wubios.wustl.edu>
Sent: Fri, June 4, 2010 8:56:29 AM
Subject: RE: [S] quasi-monte carlo integration for mixed discrete and continuous variables

n_1000
mn_0
std_1
x<-sample(c(-1,1), n, replace=T)
y<-abs(rnorm(n,mn,std))
z<-x*y

where the distribution of z is normal but the sign depends on the binary

-----Original Message-----
From: s-news-owner <at> lists.biostat.wustl.edu [mailto:s-news-owner <at> lists.biostat.wustl.edu] On
Behalf Of Wei Ye
Sent: Friday, June 04, 2010 11:38 AM
To: Robert A LaBudde
Cc: s-news <at> wubios.wustl.edu
Subject: Re: [S] quasi-monte carlo integration for mixed discrete and continuous variables

Thank you.

It is  not a problem to generate the points for either a binary variable or a continuous variable. My question
is how to generate the points for both a binary variable and a continuous variable with the latter
distribution depending on the value of the former.

----- Original Message ----
From: Robert A LaBudde <ral <at> lcfltd.com>
To: Wei Ye <weiye1 <at> yahoo.com>
Cc: s-news <at> wubios.wustl.edu
Sent: Fri, June 4, 2010 2:14:05 AM
Subject: Re: [S] quasi-monte carlo integration for mixed discrete and continuous variables

Quasirandom sequences typically fall in [0,1]. You can model either continuous or discrete variables by
scaling (continuous) or decision-point discretization (discrete). E.g., for a binary variable, code
"0" for the quasirandom number < 0.5 and "1" if greater.

I would suggest Halton or Hammersley sequences as an initial method to consider.

At 01:22 AM 6/4/2010, Wei Ye wrote:
> Hi, all,
>
> Are there any low-discrepancy sequences to do quasi-monte carlo integration for a multivariate
distribution with both discrete and continuous variables?
> For example, the continuous variable (A) follows a normal distribution and the discrete variable (B) is
binary. The mean of  the normal distribution  depends on the value of B.
>
> Can S-plus generate such sequences?
>
> Thanks.
>
>
>

================================================================
Robert A. LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral <at> lcfltd.com
Least Cost Formulations, Ltd.            URL: http://lcfltd.com/
824 Timberlake Drive                     Tel: 757-467-0954
Virginia Beach, VA 23464-3239            Fax: 757-467-2947

"Vere scire est per causas scire"
================================================================

--------------------------------------------------------------------
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```
4 Jun 2010 22:18

### Re: quasi-monte carlo integration for mixed discrete and continuous variables

```At 02:05 PM 6/4/2010, Wei Ye wrote:
>Thanks. But I am asking how to generate quasi monte carlo points,
>not pseudo random points.
>
>Quasi monte carlo points are deterministic.

1. Pseudorandom numbers are also deterministic.

2. To use a Halton sequence:

x1<- (1:N - 0.5)/N
w<- (1:N)*sqrt(2.)
x2<- w - trunc(w)

Now x1 and x2 are the 2-D quasirandom sequence you need.

================================================================
Robert A. LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral <at> lcfltd.com
Least Cost Formulations, Ltd.            URL: http://lcfltd.com/
824 Timberlake Drive                     Tel: 757-467-0954
Virginia Beach, VA 23464-3239            Fax: 757-467-2947

"Vere scire est per causas scire"
================================================================

--------------------------------------------------------------------
This message was distributed by s-news <at> lists.biostat.wustl.edu.  To
unsubscribe send e-mail to s-news-request <at> lists.biostat.wustl.edu with
the BODY of the message:  unsubscribe s-news

```
4 Jun 2010 22:29

### Re: quasi-monte carlo integration for mixed discrete and continuous variables

```Sorry: This wasn't a real Halton sequence, as the sqrt(2) sequence is
not the right way to do this (it should really be a reflection of the

However, the 2-D sequence as given is still a quasirandom sequence.

To generate real Halton or Hammersley sequences would require
somewhat more programming.

A very good set of 2-D quasirandom low discrepancy numbers is

F3<- 1
F2<- 1
for (i in 1:?) {
F1<- F2 + F3
}
x1<- 1:F1/F1
w<- (1:F1)*F3/F1
x2<- w - trunc(w)

i.e., the F's are the Fibonacci sequence.

At 04:18 PM 6/4/2010, Robert A LaBudde wrote:
>At 02:05 PM 6/4/2010, Wei Ye wrote:
>>Thanks. But I am asking how to generate quasi monte carlo points,
>>not pseudo random points.
>>
>>Quasi monte carlo points are deterministic.
>
>1. Pseudorandom numbers are also deterministic.
>
>2. To use a Halton sequence:
>
>x1<- (1:N - 0.5)/N
>w<- (1:N)*sqrt(2.)
>x2<- w - trunc(w)
>
>Now x1 and x2 are the 2-D quasirandom sequence you need.
>
>================================================================
>Robert A. LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral <at> lcfltd.com
>Least Cost Formulations, Ltd.            URL: http://lcfltd.com/
>824 Timberlake Drive                     Tel: 757-467-0954
>Virginia Beach, VA 23464-3239            Fax: 757-467-2947
>
>"Vere scire est per causas scire"
>================================================================
>
>--------------------------------------------------------------------
>This message was distributed by s-news <at> lists.biostat.wustl.edu.  To
>unsubscribe send e-mail to s-news-request <at> lists.biostat.wustl.edu with
>the BODY of the message:  unsubscribe s-news

================================================================
Robert A. LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral <at> lcfltd.com
Least Cost Formulations, Ltd.            URL: http://lcfltd.com/
824 Timberlake Drive                     Tel: 757-467-0954
Virginia Beach, VA 23464-3239            Fax: 757-467-2947

"Vere scire est per causas scire"
================================================================

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```
5 Jun 2010 16:04

### Peter Hingley is out of the office.

```
I will be out of the office starting  05-06-2010 and will not return until
15-06-2010.

I will respond to your message when I return.

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9 Jun 2010 00:23

### Breuschâ€“Godfrey

Is there a Breusch–Godfrey test available within S-Plus anywhere? If so, where is it and what's it called. I poked around a bit and didn't find it.
--
Kim Elmore, Ph

Kim Elmore, Ph.D. (PP SEL/MEL/Glider, N5OP, 2nd Class Radiotelegraph, GROL)

“There is no such thing as bad weather, only different kinds of good weather.” – John Ruskin

Attachment (kim_elmore.vcf): text/x-vcard, 382 bytes
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9 Jun 2010 15:05

### Re: Breusch?Godfrey

Kim,
That test is available in the lmtest package for R. I do not know about S.
Dave

 From: Kim Elmore noaa.gov> To: s-news lists.biostat.wustl.edu Date: 06/08/2010 05:27 PM Subject: [S] Breusch–Godfrey Sent by: s-news-owner lists.biostat.wustl.edu

Is there a Breusch–Godfrey test available within S-Plus anywhere? If so, where is it and what's it called. I poked around a bit and didn't find it.
--

Kim Elmore, Ph.D. (PP SEL/MEL/Glider, N5OP, 2nd Class Radiotelegraph, GROL)

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Gmane