Re: [R] Incomplete Factorial design

Spencer Graves Fri, 06 Feb 2004 09:11:09 -0800

Hi, Simon: Excellent observation, reinforcing the point that interpretation of confounded effects depends on the context.

     Best Wishes,
     spencer graves

Simon Fear wrote:

One could also fit

fit <- lm(y~A*B - 1, data.frame(y=..., A=..., B=..,)
which will give a direct a:b term (as the negative of the
intercept in Spenser's formulation). Arguably this is more
natural in a setting where there is no placebo so that
an intercept term has a less obvious interpretation.
-----Original Message-----
From: Spencer Graves [mailto:[EMAIL PROTECTED]
Sent: 06 February 2004 14:39
To: [EMAIL PROTECTED]
Cc: [EMAIL PROTECTED]
Subject: Re: [R] Incomplete Factorial design
Security Warning: If you are not sure an attachment is safe to open please contact Andy on x234. There are 0 attachments with this message. ________________________________________________________________

I assume that means you have two treatments, say A and B, can be either absent or present. The standard analysis codes them as -1 or +1 for absent or present, respectively. If you have observations in all 4 cells, you can write the following equation:

y(A,B) = b0 + b1*A + b2*B + b12*A*B + error.

This equation has 4 unknowns, b1, b1, b2 and b12. If you have all 4 cells in the 2x2 table, then you can estimate all 4 unknowns. If you have data for only 3 cells, the standard analysis pretends that b12 = 0 and estimates the other three. If you have only 2 cells, say (both absent) and (both present), the standard analysis can estimate b0 plus either of b1 or b2. However, in fact, these really estimate (b0+b12) and (b1+b2). To understand this, consult any good book that discusses confounding with 2-level fractional factorial designs.

To do this in R, use "lm", as

fit <- lm(y~A+B, data.frame(y=..., A=..., B=..,)

hope this helps. spencer graves

[EMAIL PROTECTED] wrote:

Hello, I am planning a study with the main point to evaluate the

interaction of two treatments,

but for ethical reasons one cell is empty, that with

patients receaving no treatment at all
Treatment B + -
Treatment A
+
a
b
- c -------

I am looking for functions in R to estimate the sample size
and/or to conduct the

analysis. I have just found an article from Byar in

Statistics in Medicine for a 2^3

incomplete factorial design, but I would like not to

discover again the wheel..
TIA
dr. Giovanni Parrinello
Section of Medical Statistics
Department of Biosciences
University of Brescia
25127 Viale Europa, 11
Brescia Italy
Tel: +390303717528
Fax: +390303701157
[[alternative HTML version deleted]]

______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html

______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html

Simon Fear Senior Statistician Syne qua non Ltd Tel: +44 (0) 1379 644449 Fax: +44 (0) 1379 644445 email: [EMAIL PROTECTED] web: http://www.synequanon.com Number of attachments included with this message: 0 This message (and any associated files) is confidential and\...{{dropped}}

______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html


______________________________________________
[EMAIL PROTECTED] mailing list
https://www.stat.math.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html

Re: [R] Incomplete Factorial design

Reply via email to