similar to: BIC doesn't work for glm(family=binomial()) (PR#8208)

Full_Name: Ju-Sung Lee Version: 1.9.1 OS: Windows XP Submission from: (NULL) (128.2.79.102) The following commands causes R to crash: a = array(1:3) dimnames(a)[[1]] = c('1','2','3') cor(a,matrix(1:3)) not naming the array seems to work.

Hi! The nobs() method for glm objects always returns the number of cases with non-null weights in the data, which does not correspond to the number of observations for Poisson regression/log-linear models, i.e. when family="poisson" or family="quasipoisson". This sounds dangerous since nobs() is, as the documentation states, primarily aimed at computing the Bayesian

Hi All, Could someone help me decode what this error means ? > BIC(nb.80) Error in log(attr(object, "nobs")) : Non-numeric argument to mathematical function > BTW, nb.80 is a negative binomial glm model created using the MASS library with the call at the bottom of the message In the hopes of trying to figure this out I tried the following workaround but it did not work

Hi, Currently the `gaussian' family's initialization code signals an error if any response data are zero or negative and a log link is used. Given that zero or negative response data are perfectly legitimate under the GLM fitted using `gaussian("log")', this seems a bit unsatisfactory. Might it be worth changing it? The current offending code from `gaussian' is:

i added the following in ~/.wine/user.reg [SoftwareWineFontsReplacements] "System"="AR PL New Sung" "Arial"="AR PL New Sung" "Fixedsys"="AR PL New Sung" "Microsoft Sans Serif"="AR PL New Sung" "MS UI Gothic"="AR PL New Sung" "Tahoma"="AR PL New Sung"

>>>>> "MM" == Martin Maechler <maechler at stat.math.ethz.ch> >>>>> on Tue, 18 May 2010 12:37:21 +0200 writes: >>>>> "GaGr" == Gabor Grothendieck <ggrothendieck at gmail.com> >>>>> on Mon, 17 May 2010 09:45:00 -0400 writes: GaGr> BIC seems like something that would logically go into stats

Dear all, I am studying a bit the various support functions that exist for extracting information from fitted model objects. From the help files it is not completely clear to me whether the number returned by nobs() should be the same as the "nobs" attribute of the object returned by logLik(). If so, then there is a slight inconsistency in the methods for 'nls' objects with

Hi all, The nobs method of (MASS:::polr class) takes into account of weight, but nobs method of glm does not. I wonder what is the rationale of such design behind nobs.glm. Thanks in advance. Best Regards. > library(MASS) > house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) > house.logit <- glm(I(Sat=='High') ~ Infl + Type + Cont, binomial,weights

I've been trying to figure out how to accomplish the following... I've got a list (returned from a function) and I would like to "cbind()" the lists together to create a "cross tab" report or simply bind them together somehow the function returns a list that looks like the following: > all$BM $species [1] "BM" $vbar.nobs [1] 3 $vbar.sum [1] 54.05435

The function 'regsubsets' appears to calculate a BIC value that is different from that returned by the function 'BIC'. The latter is explained in the documentation, but I can't find an expression for the statistic returned by 'regsubsets'. Incidentally, both of these differ from the BIC that is given in Ramsey and Schafer's, The Statistical Sleuth. I assume

Hi, in R-3.0 I get the following error when calling lsfit with more observations than variables, which seems to come from an error in the formatting of the error message (note that this was not happening in 2.15.3): > nobs <- 5; nvar <- 6; lsfit(matrix(runif(nobs*nvar), ncol=nvar), runif(nobs), intercept=FALSE) Error in sprintf(ngettext(nry, "%d response", "%d

Greetings to r-help land. I've run into some program crashes and I've traced them back to methods() behavior after the package gdata is loaded. I provide now a minimal re-producible example. This seems bugish to me. How about you? dat <- data.frame(x = rnorm(100), y = rnorm(100)) lm1 <- lm(y ~ x, data = dat) methods(class = "lm") ## OK so far library(gdata)

Dear list Having just started to use the Windows version, I am very impressed with it's package handling as well as the gui. So I tried to see what was due for update and packages such as Hmisc, Matrix and others came up. But when I had updated them - which took a few goes as something hung between here and Bristol - I noticed that the default packages such as nmle, MASS had disappeared. I

Dear R friends: Can someone help me with the following problem? Many thanks in advance. # Problem Description: # I want to write functions which take a (character) vector of dataframe names as input argument. # For example, I want to extract the number of observations from a number of dataframes. # I tried the following: nobs.fun <- function (dframe.vec) { nobs.vec <-

It seems that confint.default returns an empty data.frame for objects of class mlm. For example: ``` nobs <- 20 set.seed(1234) # some fake data datf <- data.frame(x1=rnorm(nobs),x2=runif(nobs),y1=rnorm(nobs),y2=rnorm(nobs)) fitm <- lm(cbind(y1,y2) ~ x1 + x2,data=datf) confint(fitm) # returns: 2.5 % 97.5 % ``` I have seen proposed workarounds on stackoverflow and elsewhere, but

Hi, i have still the same problem. I use 3.0RC3 with lib/charcnv.c of the actual CVS (Sep 10 15:19). I works not realy: I can create (Windows XP + Explorer) the filename "L?sung.txt" but not "?sung.txt". -- Greetings M. Ungermann

Hi all, I have a very simple question that I can't seem to find the answer to. How do I extract rows that meet a certain criteria from a data frame and group them into a new data frame? For example, if I want to make a new data frame that only includes rows of data for which the p values (given by one of the columns in the data frame) are less than a certain value, how do I do this? It seems

I was wondering if it is possible to do the following in a smarter way. I want get the mean value across the columns of a matrix, but I want to do this on subrows of the matrix, given by some vector(same length as the the number of rows). Something like nObs<- 6 nDim <- 4 m <- matrix(rnorm(nObs*nDim),ncol=nDim) fac<-sample(1:(nObs/2),nObs,rep=T) ##loop trough different

Hello, how is, for example, the Schwarz criterion is defined for kmeans? It should be something like: k <- 2 vars <- 4 nobs <- 100 dat <- rbind(matrix(rnorm(nobs, sd = 0.3), ncol = vars), matrix(rnorm(nobs, mean = 1, sd = 0.3), ncol = vars)) colnames(dat) <- paste("var",1:4) (cl <- kmeans(dat, k)) schwarz <- sum(cl$withinss)+ vars*k*log(nobs) Thanks

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