Reading the help often resolves questions, which is why we ask you to do
so before posting. From ?summary.manova:
Usage:
## S3 method for class 'manova':
summary(object,
test = c("Pillai", "Wilks", "Hotelling-Lawley", "Roy"),
intercept = FALSE, ...)
Arguments:
object: An object of class '"manova"' or an 'aov' object with
multiple responses.
test: The name of the test statistic to be used. Partial matching
is used so the name can be abbreviated.
...
The 'summary.manova' method uses a multivariate test statistic for
the summary table. Wilks' statistic is most popular in the
literature, but the default Pillai-Bartlett statistic is
recommended by Hand and Taylor (1987).
Note, not mention of 'tests' and explicitly 'a ... test'. It is a
standard convention that when several alternatives are given in the usage
the first is the default, _and_ the text does say this explicitly.
(I might have followed up your next question had you given a URL link to
the posting concerned.)
On Thu, 3 Apr 2008, Ray Haraf wrote:
>
> I would be very appreciative of your help with the following
>
> 1). I am running multivariate multiple regression through the manova()
> function (kindly suggested by Professor Venables) and getting two
> different answers for test=c("Wilks","Roy","Pillai") and
> tests=c("Wilks","Roy",'"Pillai") as shown below. In the first case
> (test=c(list)) I got error message which probably means I can only call
> one test at a time. I thought I could get ride of this by adding "s" to
> test; in this case (tests=c(list)), I got Pillai test. Does this mean
> that Pillai would be the default test and summary(manova()) can only
> post one test at a time?
>
>> summary(manova(cbind(y1, y2) ~ z1, data =
> + ex7.8),test=c("Wilks","Roy","Pillai"))
> Error in match.arg(test) : 'arg' must be of length 1
>> summary(manova(cbind(y1, y2) ~ z1, data =
> + ex7.8),tests=c("Wilks","Roy","Pillai"))
> Df Pillai approx F num Df den Df Pr(>F)
> z1 1 0.9375 15.0000 2 2 0.0625 .
> Residuals 3
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> 2). My next struggle is to construct prediction ellipse. Both ellipse() and
> ellipse.lm() are not giving me the solution to "Sampling from multivariate
> multiple regression prediction regions" posted by Iain Pardoe, Mon May 9
> 18:43:46 2005. I am working on the same problem and
> performed all the steps he suggested
>
>> ex7.10 <-
> + data.frame(y1 = c(141.5, 168.9, 154.8, 146.5, 172.8, 160.1, 108.5),
> + y2 = c(301.8, 396.1, 328.2, 307.4, 362.4, 369.5, 229.1),
> + z1 = c(123.5, 146.1, 133.9, 128.5, 151.5, 136.2, 92),
> + z2 = c(2.108, 9.213, 1.905, .815, 1.061, 8.603, 1.125))
>> attach(ex7.10)
>> f.mlm <- lm(cbind(y1,y2)~z1+z2)
>> y.hat <- c(1, 130, 7.5) %*% coef(f.mlm)
>> round(y.hat, 2)
> y1 y2
> [1,] 151.84 349.63
>> qf.z <- t(c(1, 130, 7.5)) %*%
> + solve(t(cbind(1,z1,z2)) %*% cbind(1,z1,z2)) %*%
> + c(1, 130, 7.5)
>> round(qf.z, 5)
> [,1]
> [1,] 0.36995
>> n.sigma.hat <- SSD(f.mlm)$SSD # same as t(resid(f.mlm)) %*%
> resid(f.mlm)
>> round(n.sigma.hat, 2)
> y1 y2
> y1 5.80 5.22
> y2 5.22 12.57
>> F.quant <- qf(.95,2,3)
>> round(F.quant, 2)
> [1] 9.55
>
>
>> From here how could I calculate a 95% prediction ellipse for y=(y1,y2) at
>> (z1,z2)=(130,7.5) using either ellipse or ellipse.lm? y1 would be the x-axis
>> and y2, the y-axis. The center is different from (0,0) and I don't know what
>> would be the appropriate x (the lm object). Should I
> used predicted values or residuals? In both cases I have vectors which is
> different from the example given with ellipse.lm
>
> 3). Lastly but not the least, would be too ambitious to draw the axes (i.e,
> the eigenvalues) to the ellipse?
>
> Thanks and very kind regards,
> Ray
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> [email protected] 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.
>
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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