similar to: bug in glm.fit (PR#395)

Dear R-team As I didn't get any answer to my bug-report last week I have taken the effort and extracted a minimal data set from my data (see below) where the following bug occurs: > glm(SKR.ein.aus ~ ., family = binomial, data = bugdata, na.action = na.omit) Error in names<-.default(*tmp*, value = ynames) : names attribute must be the same length as the vector In addition: Warning

Dear R users, I don't understand, what happens in glm in the following example (note that in S-Plus this example finishes with an almost perfect fit, but also 49 warnings): > fit.small <- glm(SKR.ein.aus ~ ., family = binomial, data = daten, maxit=100) Error in (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y, : inner loop 2; can't correct step size In addition:

G'day all, I had a look at the GLM code of R (1.4.1) and I believe that there are problems with the function "glm.fit" that may bite in rare circumstances. Note, I have no data set with which I ran into trouble. This report is solely based on having a look at the code. Below I append a listing of the glm.fit function as produced by my system. I have added line numbers so that I

Hi all: I get the following error message that I am not able to resolve. Error in if (const(t, min(1e-08, mean(t)/1e+06))) { : missing value where TRUE/FALSE needed It appears right before the last data.frame statement. Below is the program that simulates data from one way random effects model and then computes normality and bootstrap confidence interval for

I'm not understanding something. I'm trying to add xlab & ylab to a balloon plot of a table object. From docs I thought following should work: require(gplots) # From balloonplot example: # Create an example using table xnames <- sample( letters[1:3], 50, replace=2) ynames <- sample( 1:5, 50, replace=2) tab <- table(xnames, ynames) balloonplot(tab)

mingw, llvm 2.6 (buid with llvm-gcc) Example source code: http://npcontemplation.blogspot.com/2008/06/secret-of-llvm-c-bindings.html I change LLVMCreateJITCompiler(&engine, provider, &error); to LLVMCreateJITCompiler(&engine, provider, 3, &error); $ llvm-gcc `llvm-config --cflags` -c fac.c $ g++ `llvm-config --libs --cflags --ldflags core analysis executionengine jit

I've got a complicated semi-parametric model that I'm fitting with mgcv. I start with a model based on theory. Its got lots of interaction terms. I want to winnow it down: removing each interaction term or un-interacted main effect one by one, checking the AIC, and retaining the model that gives me the lowest AIC. I then want to repeat the procedure on the retained model. Here is

Hi, I am analyzing a data set with greater than 1000 independent cases (collected in an unrestricted manner), where each case has 3 variables associated with it: one, a factor variable with 0/1 levels (called XX), another factor variable with 8 levels (X) and a third response variable with two levels (Y: 0/1). I am trying to see if X1 has an effect on the relationship between X2 and the

Hi, I'm trying to get sparse.model.matrix to retain unused levels. I can't seem to get this working through the most obvious routes such as specifying drop.unused.levels = FALSE in the model.frame or trying to pass all levels in xlev,which is an argument to sparse.model.matrix (see code below). Any help would be gratefully received. Cheers, Jarrod fac<-factor(rep(1:10,10),

Dear all, I'm using the mgcv library by Simon Wood to fit gam models with interactions and I have been reading (and running) the "factor 'by' variable example" given on the gam.models help page (see below, output from the two first models b, and b1). The example explains that both b and b1 fits are similar: "note that the preceding fit (here b) is the same as

Hi, I need to resample rows in a data frame by subsets L3 <- LETTERS[1:3] d <- data.frame(cbind(x=1, y=1:10), fac=sample(L3, 10, repl=TRUE)) x y fac 1 1 1 A 2 1 2 A 3 1 3 A 4 1 4 A 5 1 5 C 6 1 6 C 7 1 7 B 8 1 8 A 9 1 9 C 10 1 10 A I have seen this used to sample rows with replacement d[sample(nrow(d), replace=T), ] x y fac 7 1 7 B 2

Hello useRs, I'm pleased to announce the general availability of the R package futile.paradigm, which is a language extension that implements functional dispatching in R. This is an alternative to the current object-oriented styles, replacing them with a functional programming style that provides a clean, fine-grained declarative syntax for function definitions. The core of the package

Hello useRs, I'm pleased to announce the general availability of the R package futile.paradigm, which is a language extension that implements functional dispatching in R. This is an alternative to the current object-oriented styles, replacing them with a functional programming style that provides a clean, fine-grained declarative syntax for function definitions. The core of the package

Dear list-member, I am currently developing a package with S4 classes. The NAMESPACE and DESCRIPTION is printed below. Within this package I have set a method "residuals" for two classes. In version 2.8.1 these two are reported whereas in R-Devel (2009-01-28 r47766). What have I missed? What has changed and how can I rectify the issue? Your help and pointers are welcome. For 2.8.1:

[I originally posted this on the R-help mailing list, and it was suggested that R-devel would be a better place to dicuss it.] Running ?table? on a factor with levels containing non-ASCII characters seems to result in extremely bad performance on Windows. Here?s a simple example with benchmark results (I?ve reduced the number of replications to make the function finish within reasonable time):

Hello List Folks. I?m trying to do manova using a function which assembles the response (LHS) and factors (RHS) from different sources. When I do so, the model routines complain that I'm passing a list for the RHS when I don't think I am. Here's a toy example: r <- matrix(rnorm(30), ncol = 3) # toy data td <- list(d = rnorm(10), f1 = sample(c("A",

Hi all, I noticed something strange when I ran aov and anova. vtot=c(7.29917, 7.29917, 7.29917) #identical values fac=as.factor(c(1,1,2)) #group 1 has first two elements, group 2 has the 3rd element When I run: > anova(lm(vtot~fac)) Analysis of Variance Table Response: vtot Df Sum Sq Mean Sq F value Pr(>F) fac 1 1.6818e-30 1.6818e-30 0.3333 0.6667 Residuals 1

Hi I'm doing a glm analysis and I have two doubts (at least :) 1) When I run the function it gives a lot of warnings (see below) what they mean ? (may be I'm ignorant about this analysis ...) glm.poisson<-glm(log(Jkij+1)~fac.ano+fac.tri+fac.icesr+fac.mat+fac.ano:fac.icesr+fac.ano:fac.tri,family=poisson()) warnings() 40: non-integer x = 1.252763 41: non-integer x = 1.864785 42:

Hi, what would be a good way of enhancing the ancova function from the HH package when using a 2 factor ANCOVA? The current behaviour for the "ancova" function from package HH is: ---------------------------------------------- | P1 || P1 || P3 || P4 | | PS | <- the lattice strip ---------------------------------------------- | x|| x ||x ||x | |

Suppose I have a dataframe 'd' defined as L3 <- LETTERS[1:3] d0 <- data.frame(cbind(x = 1, y = 1:10), fac = sample(L3, 10, replace = TRUE)) (d <- d0[d0$fac %in% c('A', 'B'),]) x y fac 2 1 2 B 3 1 3 A 4 1 4 A 5 1 5 A 6 1 6 B 8 1 8 A Even though factor 'fac' in 'd' only has 2 levels, but it seems to bear the birthmark