similar to: anova() on three or more objects behaves inconsistently (PR#621)

Dear all, I noticed the following while playing around with fitting log-linear models to contingency tables using R 1.8.1, but the problem also exists under R 1.9.0. A reproducible example uses the following contingency table: > library(MASS) > data(quine) > tmp <- with(quine, expand.grid(Eth=levels(Eth), Sex=levels(Sex), + Lrn=levels(Lrn), Age=levels(Age)))

Hello, I'm working in adding support for 64-bit integers to my target. I'm using LLVM to decompose the 64-bit integer operations by using 32-bit registers wherever possible and emulating support where not. When looking at the bit shift decomposition I saw what seems to be a bug in the implementation. The affected function is ExpandShiftWithUnknownAmountBit in LegalizeIntegerTypes.cpp.

Hi Folks, I'm playing with glm.nb() in MASS. Reference: the negative binomial distribution P(y) = (Gamma(theta+y)/(Gamma(theta)*y!))*(p^theta)*(1-p)^y y = 0,1,2,... in the notation of the MASS book (section 7.4), where p = theta/(mu + theta) so (1-p) = mu/(mu + theta) where mu is the expected value of Y. It seems from ?glm.nb that an initial value of theta is either supplied, or

The code below is a small reproducible example of a much larger problem. While the script below works, it is really slow on the true dataset with many more rows and columns. I'm hoping to get the same result to examp, but with significant time savings. The example below is setting up a data.frame for an ensuing regression analysis. The purpose of the script below is to appends columns to

predict.lm has been broken by recent changes to the patched branch. It fails for all singular fits. An example: library(MASS) data(quine) quine.hi <- aov(log(Days + 2.5) ~ .^4, quine) quine.nxt <- update(quine.hi, . ~ . - Eth:Sex:Age:Lrn) predict(quine.nxt) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 NA NA NA NA NA NA NA NA NA NA NA NA NA

Some background: I have some data on structural dependencies in a base of code artifacts. The dependency structure is reflected in terms of relative node degrees, with each node representing some code unit (just as an example). This gives me real data of the following form (sorry for the longish posting): dat1 <- c(0.00245098039215686, 0, 0, 0, 0, 0, 0, 0, 0.0563725490196078, 0, 0, 0,

Hi Stephen, > Hi all, llvm newbie here. welcome! > I'm trying to branch based on a vector compare. I've found a slow way (below) > which goes through memory. Is there some idiom I'm missing so that it would use > for instance movmsk for SSE or vcmpgt & cr6 for altivec? I don't think you are missing anything: LLVM IR has no support for horizontal operations like

Dear all, I would like to Boostrap the stepAIC() procedure from package MASS for variety of model objects, i.e., fn <- function(object, data, B = 2){ n <- nrow(data) res <- vector(mode = "list", length = B) index <- sample(n, n * B, replace = TRUE) dim(index) <- c(n, B) for (i in 1:B) { up.obj <- update(object, data = data[index[, i], ])

Summary: loglin will fail if there is a global variable called c. I have fixed this for 0.64.2 and 0.65. On Sat, 22 May 1999, Cor en Aylin wrote: > Dear Brian, > > Using the mass library for R (the most recent port dated 16 may 1999) I Could you give version numbers in such reports please: at the moment there is an official version (5.3pl037) of MASS for R, plus a pre-release of MASS

Dear list, This is a question about overdispersion and the ML estimates of the parameters returned by the glm.poisson.disp (L. Scrucca) and glm.nb (Venables and Ripley) functions. Both appear to assume a negative binomial distribution for the response variable. Paul and Banerjee (1998) developed C(alpha) tests for "interaction and main effects, in an unbalanced two-way layout of counts

On Mon, Nov 30, 2009 at 7:22 PM, Javier Martinez <javier at jmartinez.org> wrote: > Hello, > > I'm working in adding support for 64-bit integers to my target. I'm using > LLVM to decompose the 64-bit integer operations by using 32-bit registers > wherever possible and emulating support where not. When looking at the bit > shift decomposition I saw what seems to be a

Hi, I have a dataset with the following properties: Y_i ~ N(mu_i, theta * (mu_i)^2) ln(mu_i) = B'Xi theta and beta's are the parameters here. I want to come up with a model to fit the data with the above property and test that model on the built in R dataset quine. Does nls() make sense in this case? Or is there any existing R package which can fit this model? -Shelly -- View

Does anybody know of aything that will help me do Quantitiative Comparative Analysis (QCA) and/or Fuzzy set analysis?? Or failing that Quine? ta rg Prof R Gott Durham Univesrity UK

Is there an R version of the very handy S-PLUS function spin? I've tried the help pages and apropos() without success. Malcolm Quine -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the

Hi, I want to use the value of variable to quote elements in matrix. For example, I have a matrix like:               y1   y2m1         1      2m2         3      4 where y1,y2,m1,m2 are column and row names.  I have two random character variable, say x,  that could be either  y1 or y2  and  y that could be either m1 or m2.  So can I  do like   Matrix[y,x] to quote elements?  I've tried this

Dear list members, I am pleased to let you know that R met with QCA - Qualitative Comparative Analysis. This package has a few functions that implement the Quine-McCluskey algorithm, adapted to social sciences by Charles Ragin (as describes in his book from 1987 "The Comparative Method"). Future versions of this package will have more functions to address the fuzzy-set minimization

Dear list members, A new version of the QCA package is now on CRAN. The QCA package implements the Quine-McCluskey algorithm for boolean minimizations, according to the Qualitative Comparative Analysis. Along with the additional improvements in version 0.3-1 (soon to be released on CRAN), this code is about 100 times faster than the previous "major" release (0.2-6). It can now

Dear list members, A new version of the QCA package is now on CRAN. The QCA package implements the Quine-McCluskey algorithm for boolean minimizations, according to the Qualitative Comparative Analysis. Along with the additional improvements in version 0.3-1 (soon to be released on CRAN), this code is about 100 times faster than the previous "major" release (0.2-6). It can now

Hi, A new version of the QCA package (0.6-0) was submitted to CRAN. This is a major improvement, now working with multi-valued data (previous versions accepted binary data only). The "classical" function qmcc() still accepts only binary data, but the enhanced function eqmcc() is now ready for mvQCA. The QCA package performs the Quine-McCluskey algorithm for Qualitative Comparative

Hi, A new version of the QCA package (0.6-0) was submitted to CRAN. This is a major improvement, now working with multi-valued data (previous versions accepted binary data only). The "classical" function qmcc() still accepts only binary data, but the enhanced function eqmcc() is now ready for mvQCA. The QCA package performs the Quine-McCluskey algorithm for Qualitative Comparative