similar to: Error in storage when attempting to run GLM

Hi, I wish to write a new link function for a GLM. R's glm routine does not supply the "loglog" link. I modified the make.link function adding the code: }, loglog = { linkfun <- function(mu) -log(-log(mu)) linkinv <- function(eta) exp(-exp(-eta)) mu.eta <- function(eta) exp(-exp(-eta)-eta) valideta <- function(eta) all(eta != 0)

At useR 2006 I mentioned that it would be nice to have a way to specify binomial links that involved free parameters and described some experience with a Gosset link involving a free degrees of freedom parameter, and a Tukey-lambda link with two free parameters. My implementation of this involved some rather kludgey modifications of binomial, make.link and glm that (essentially) added a

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

I am going to fit my model with R-2.5.1.tar my model is y=fixed effect+b(x) and I have three column in my data file (y a x) and about 200000 observation(y) but I had some problems. I was wondering If you could help me the error massage is: > res <- read.table("pro.sdf") > fm <- glm(y=A+x,data=res) Error: NA/NaN/Inf in foreign function call (arg 4) In addition: Warning

Greetings, everyone. I've been trying to analyze bird nest survival data using generalized linear mixed models (because we documented several consecutive nesting attempts by the same individuals; i.e. repeated measures data) and have been unable to persuade the various GLMM models to work with my user-defined link function. Actually, glmmPQL seems to work, but as I want to evaluate a suite of

Using zero-weight values in glm returns incorrect fitted values and linear predictors, the ninth value in the following. > example(glm) > fit <- glm(counts ~ outcome + treatment, family = poisson(), data=d.AD, weights=c(rep(1,8), 0)) > fit$linear.predictor 1 2 3 4 5 6 7 8 2.989646 2.535391 2.862201 2.989646

Dear R-Users, I wish to create a new family object based on the Binomial family. The only difference will be with the link function. Thus instead if using the 'logit(u)' link function, i plan to use '-log(i-u)'. So far, i have tried to write the function following that of the Binomial and Negative Binomial families. The major problem i have here is with the definition of the

FWIW p. 206 of the White Book gives the following for names(binomial()): family, names, link, inverse, deriv, initialize, variance, deviance, weight. So $aic wasn't there In The Beginning. I haven't done any more archaeology to try to figure out when/by whom it was first introduced ... Section 6.3.3, on extending families, doesn't give any other relevant info. A patch for

I am analysing parasite egg count data and am having trouble with glm with a negative binomial family. In my first data set, 55% of the 3000 cases have a zero count, and the non-zero counts range from 94 to 145,781. Eventually, I want to run bic.glm, so I need to be able to use glm(family= neg.bin(theta)). But first I ran glm.nb to get an estimate of theta: > hook.nb<- glm.nb(fh,

I am unfamiliar with R and I’m trying to do few statistical things like GLM and GAM with it. I hope my following questions will be clear enough: My datas ( y(i,j ))are run off triangles for example : J=1 J=2 J=3 I=1 1 2 3 I=2 4 5 I=3 6 My model is : E[y(i,j)] =m(i,j) Var[y(i,j)] =constant *m(i,j) Log(m(i,j)) = eta (i,j) eta (i,j) = c + alpha(i)

I'm having a hard time figuring out how to group results by certain factors in R. I have data with the following headings: [1] "Time" "Plot" "LatCat" "Elevation" "ElevCat" "Aspect" "AspCat" "Slope" [9] "SlopeCat" "Species" "SizeClass" "Stems" and

Hello to everyone. I need your help. I´m trying to fit the same *glm.nb* to a different data set and i am getting these errors in some of the data. Sometimes, one data set has two of these errors when fitting the model. 1.- Error en while ((it <- it + 1) < limit && abs(del) > eps) { : valor ausente donde TRUE/FALSE es necesario 2.- Mensajes de aviso perdidos 1: In sqrt(1/i)

Dear list, I have recently encountered an odd error when running glm(dep~indep, quasipoisson): while, with a subset of my data, I could get a perfectly reasonable model, once I include all of my data (17K+ observations, 29 variables), I get the following error: Error in if (any(y < 0)) stop("negative values not allowed for the quasiPoisson family") : missing value where

Hi Folks, I am interested in extending the repertoire of link functions in glm(Y~X, family=binomial(link=...)) to include a "tan" link: eta = (4/pi)*tan(mu) i.e. this link bears the same relation to the Cauchy distribution as the probit link bears to the Gaussian. I'm interested in sage advice about this from people who know their way aroung glm. >From the surface, it looks

Greetings: I'm running R2.6.2 on a WinXP DELL box with 2 gig RAM. I have created a new glm link function to be used with family = binomial. The function works (although any suggested improvements would be welcome), logit.FC <- function(POD.floor = 0, POD.ceiling =1) { if (POD.floor < 0 | POD.floor > 1) stop ("POD.floor must be between zero and one.") if

Is the following difference between S+ and R normal or is it a bug? Thanks. FC. PS: S+ 4.5 for Windows and R 0.63.1 for Windows. S-PLUS output: > inverse.gaussian()$deriv > function(mu) > -2/mu^3 R output: > inverse.gaussian()$deriv > NULL -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Here's a patch that should make predict.glm(..., type="response") retain the names. The change passes make check on our Opteron running SLES9. One simple test is: names(predict(glm(y ~ x, family=binomial, data=data.frame(y=c(1, 0, 1, 0), x=c(1, 1, 0, 0))), newdata=data.frame(x=c(0, 0.5, 1)), type="response")) which gives [1]

Is it generally known/has it been previously discussed here that the $aic() component in GLM-family objects (e.g. results of binomial(), poisson(), etc.) does not as implemented actually return the AIC, but rather -2*log-likelihood + 2*(model_has_scale_parameter) ? Can anyone in this forum gauge how a documentation patch would be received? This behaviour does not seem to be documented in ?family

Greetings. Here is sample code, with some comments. It shows how I can simulate data and estimate glm with binomial family when there is no individual level random error, but when I add random error into the linear predictor, I have a difficult time getting reasonable estimates of the model parameters or the variance component. There are no clusters here, just individual level responses, so

Dear list, I'm trying to mimic the analysis of Wedderburn (1974) as cited by McCullagh and Nelder (1989) on p.328-332. This is the leaf-blotch on barley example, and the data is available in the `faraway' package. Wedderburn suggested using the variance function mu^2(1-mu)^2. This variance function isn't readily available in R's `quasi' family object, but it seems to me