[R] Incomplete Factorial design
Simon Fear
Simon.Fear at synequanon.com
Fri Feb 6 16:09:21 CET 2004
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:spencer.graves at pdf.com]
> Sent: 06 February 2004 14:39
> To: parrinel at med.unibs.it
> Cc: R-help at stat.math.ethz.ch
> 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
>
> parrinel at med.unibs.it 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]]
> >
> >______________________________________________
> >R-help at stat.math.ethz.ch 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
>
>
______________________________________________
R-help at stat.math.ethz.ch 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: Simon.Fear at synequanon.com
web: http://www.synequanon.com
Number of attachments included with this message: 0
This message (and any associated files) is confidential and\...{{dropped}}
More information about the R-help
mailing list