From: John Maindonald <john.maindonald_at_anu.edu.au>

Date: Sat 10 Sep 2005 - 22:17:01 EST

> xy$y <- rnorm(40)

> summary(aov(y~cond*time+Error(expno/cond), data=xy))

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Sat Sep 10 22:26:35 2005

Date: Sat 10 Sep 2005 - 22:17:01 EST

There are 20 distinct individuals, right? expno breaks the 20 individuals into five groups of 4, right? Is this a blocking factor?

If expno is treated as a blocking factor, the following is what you get:

> xy <- expand.grid(expno=letters[1:5],cond=letters[1:4],

+ time=factor(paste(1:2))) > xy$subj <- factor(paste(xy$expno, xy$cond, sep=":")) > xy$cond <- factor(xy$cond) > xy$expno <- factor(xy$expno)

> xy$y <- rnorm(40)

> summary(aov(y~cond*time+Error(expno/cond), data=xy))

Error: expno

Df Sum Sq Mean Sq F value Pr(>F) Residuals 4 3.59 0.90

Error: expno:cond

Df Sum Sq Mean Sq F value Pr(>F) cond 3 1.06 0.35 0.36 0.78Residuals 12 11.86 0.99

Error: Within

Df Sum Sq Mean Sq F value Pr(>F) time 1 2.27 2.27 1.38 0.26cond:time 3 3.27 1.09 0.67 0.59 Residuals 16 26.19 1.64

If on the other hand this is analyzed as for a complete randomized design, the following is the output:

> summary(aov(y~cond*time+Error(subj), data=xy))

Error: subj

Df Sum Sq Mean Sq F value Pr(>F) cond 3 1.06 0.35 0.37 0.78Residuals 16 15.46 0.97

Error: Within

Df Sum Sq Mean Sq F value Pr(>F) time 1 2.27 2.27 1.38 0.26cond:time 3 3.27 1.09 0.67 0.59 Residuals 16 26.19 1.64

On 10 Sep 2005, at 8:00 PM, Larry A Sonna wrote:

*> From: "Larry A Sonna" <larry_sonna@hotmail.com>
**> Date: 10 September 2005 12:10:06 AM
*

> To: <r-help@stat.math.ethz.ch>

*> Subject: [R] Discrepancy between R and SPSS in 2-way, repeated
**> measures ANOVA
**>
**>
**> Dear R community,
**>
**> I am trying to resolve a discrepancy between the way SPSS and R
**> handle 2-way, repeated measures ANOVA.
**>
**> An experiment was performed in which samples were drawn before and
**> after treatment of four groups of subjects (control and disease
**> states 1, 2 and 3). Each group contained five subjects. An
**> experimental measurement was performed on each sample to yield a
**> "signal". The before and after treatment signals for each subject
**> were treated as repeated measures. We desire to obtain P values
**> for disease state ("CONDITION"), and the interaction between signal
**> over time and disease state ("CONDITION*TIME").
**>
**> Using SPSS, the following output was obtained:
**> DF SumSq (Type 3) Mean Sq F
**> value P=
**>
**> COND 3 42861 14287
**> 3.645 0.0355
**>
**> TIME 1 473
**> 473 0.175 0.681
**>
**> COND*TIME 3 975 325
**> 0.120 0.947
**>
**> Error 16 43219 2701
**>
**>
**>
**> By contrast, using the following R command:
**>
**> summary(aov(SIGNAL~(COND+TIME+COND*TIME)+Error(EXPNO/COND),
**> Type="III"))
**>
**> the output was as follows:
**>
**> Df Sum Sq Mean Sq F value Pr(>F)
**>
**> COND 3 26516 8839 3.2517 0.03651 *
**>
**> TIME 1 473 473 0.1739 0.67986
**>
**> COND:TIME 3 975 325 0.1195 0.94785
**>
**> Residuals 28 76107 2718
**>
**>
**>
**> I don't understand why the two results are discrepant. In
**> particular, I'm not sure why R is yielding 28 DF for the residuals
**> whereas SPSS only yields 16. Can anyone help?
**>
**>
*

John Maindonald email: john.maindonald@anu.edu.au phone : +61 2 (6125)3473 fax : +61 2(6125)5549 Centre for Bioinformation Science, Room 1194, John Dedman Mathematical Sciences Building (Building 27) Australian National University, Canberra ACT 0200.

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Sat Sep 10 22:26:35 2005

*
This archive was generated by hypermail 2.1.8
: Sun 23 Oct 2005 - 16:48:14 EST
*