Why do you keep posting something to which you have already received an answer?
https://stat.ethz.ch/pipermail/r-help/2008-April/158662.html answered in https://stat.ethz.ch/pipermail/r-help/2008-April/158664.html and repeated at https://stat.ethz.ch/pipermail/r-help/2008-April/158787.html and https://stat.ethz.ch/pipermail/r-help/2008-April/158796.html (this message). All of these are with unwrapped lines (I wrapped the version below) and contain HTML which the posting guide asked you not to send. On Sat, 5 Apr 2008, Ray Haraf wrote: > Dear All, > > I would be very appreciative of your help with the following But you then showed 0% appreciation of the help you were given! > 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]] > > ______________________________________________ > R-help@r-project.org 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 ______________________________________________ R-help@r-project.org 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.