search for: wotherspoon

Hi, Given that R reports Type I sums of squares, isn't it a bit anachronistic that it re-orders terms in formulae? > d <- expand.grid(y=rnorm(8), + A=factor(c(1,2)), + B=factor(c(1,2)), + C=factor(c(1,2))) > summary(aov(y ~ A+B+A:B+C,data=d)) Df Sum Sq Mean Sq F value Pr(>F) A 1 8.294e-34 8.294e-34 1.027e-33 1

Hi, I would consider the calculation of r-squared in the following to be a bug, but then, I've been wrong before. It seems that R looks to see if the model contains an intercept term, and if it does not, computes r-squared in a way I don't understand. To my mind, the following are two alternative parametrizations of the same model, and should yield the same r-squared. Any insight much

Hi, This might be a minor bug, or it could be that I'm just mis-using the tools (again). If you modify the example for model.tables to introduce an NA, N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0) P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0) yield <-

...t. Sorry that I cannot give a simpler example than this, its taken me quite a while to get this far, and as my debugging efforts were going nowhere, I thought it better to throw it out to the wider community. Simon -- ***************************************************************** * Simon Wotherspoon _--_|\ * * Maths Dept, University of Tasmania / Aus \ * * Tasmania, Australia \_.--._/ * * spoon@hilbert.maths.utas.edu.au v * ********************************************************...

Hi, Is this a bug in termplot, or (once again) do I just not understand what R is really doing? I am using termplot to contruct partial residual plots, 1. For all terms at once 2. One term at a time but I get different results from these two methods. To give a concrete example, I would have thought the top and bottom rows of the plot constructed with the following code would be identical.

Hi all, Perhaps this in an inappropriate post, but I've found a bug in proj I'd like to track down a bit further before making a formal bug report. The example below shows the problem, if you change the rownames proj fails. The problem seems to be that there is a mismatch in the rownames in the qr objects constructed by aov and the rownames that proj is expecting them to have.

I believe there is a memory leak in isoreg in the current version of R, as I believe the following shows > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 120405 3.3 350000 9.4 350000 9.4 Vcells 78639 0.6 786432 6.0 392463 3.0 > for(k in 1:100) { + + y <- runif(10000) + isoreg(x,y) + } > rm(x) > rm(y) > gc() used (Mb) gc

Hi, I'm trying to analyse a hierachical design and am running into some trouble. Clearly I don't fully understand "Error" and I was hoping someone could set me straight. We measure percentage algal cover in each of 5 quadrats from each of 16 patches where 4 treatments are randomly allocated to a patch. First suppose patches are coded 1 to 16. then the following gives the

Hi, the function TukeyHSD gives incorrect results for balanced incomplete block designs, as the example below shows, but I can only half fix it. There are two problems, 1. It uses model.tables to estimate treatment means, 2. It uses the wrong standard error The first problem can be fixed using dummy.coef, if the lines > TukeyHSD.aov function (x, which = seq(along = tabs), ordered = FALSE,