similar to: Help on warning message from Neg. Binomial error during glm

Gracias! El 15 de junio de 2015, 16:54, Freddy Omar López Quintero < freddy.vate01 en gmail.com> escribió: > ?Holap.? > > ran out of iterations and failed to converge > > > ?Prueba aumentando el número de iteraciones, con el argumento maxit: > > ?GLM <- bigglm(In.hospital_death ~ GCS + BUN, data = DatosGLM, family = >> binomial(logit), maxit=1000)? >

Hi, I have make an analise with presence and absence, y=(1 e 0). I have a area continuous data and a sp data with 25 levels. I have 300 points. When I make glm((presenca/peso)~area,weights=peso,family=binomial,maxit=1000) where presenca is 0 or 1. peso is the unit = 1. area is the continuous data. The analysis is OK. When I put the sp and interactions in analysis this warning appear.

Hi, I want to fit a GEE with a negative binomial distribution. I have uesd already a poisson glm and then neg binommial to deal with alot of dispersion. In my neg binomial residuals i have some patterns so i have implemented a GEE, but only with a poisson family as i couldnt with neg binomial. However the residual patterns in fact look worse here. When i try and put neg binomial family it

Dear all, I have a puzzle regarding the estimation of Neg. Binomial event count model in R. I would greatly appreciate if anyone could shed some light on my puzzle. Using the glm.nb command, or the zelig command developed by Gary King et. al., I obtain the same point estimates in R as well as in Stata. However, if I write my own likelihood function to estimate a neg. binomial event count

Hi userRs! I am trying to fit some GLM-poisson and neg.binomial. The neg. Binomial model is to account for over-dispersion. When I fit the poisson model i get: (Dispersion parameter for poisson family taken to be 1) However, if I estimate the dispersion coefficient by means of: sum(residuals(fit,type="pearson")^2)/fit$df.res I obtained 2.4. This is theory means over-dispersion since

Hi Folks, I've just come across a kind of problem which leads me to wonder how to approach it in R. Basically, each a set of items is subjected to a series of "impacts" until it eventually "fails". The "force" of each impact would depend on covariates X,Y say; but as a result of preceding impacts an item would be expected to have a "cumulative

Hi, I'm trying to extract a histogram over the results from a bootstrap. However I keep receiving the error message "Error in hist.default(boot.lrtest$ll, breaks = "scott") : 'x' must be numeric". The bootstrap I'm running looks like: > boot.test <- function(data, indeces, maxit=20) { + y1 <- fit1+e1[indeces] + mod1 <- glm(y1 ~ X1-1, maxit=maxit) +

Dear all, When I try to return some vectors from some functions within a function, it indicate an error," Error in rbind(ck1, ck2, ck3) : object 'ck1' not found", in one of the iterations and stop.  Since I am not experienced in programming, can anyone give me a suggestion to inspect this error? The followings are the functions I created : ################### # functions in the

Hello R-listers! My first post to the list is a very simple one for those who use the software continuosly. I am trying to understand the fixed-x resampling and random-x-resampling method proposed by Fox about Bootstrapping. The doubt that I have is on the side of the model run in one of the functions expressed for fixed-x resampling. What I don't understand is: X=model.matrix, and the -1

Respected Sir I tried the strucchange My data is attached. However I tried the attached commands (last save.txt) to perform Bai Perron 2003... I t worked well but in the end it is giving warning that overlapping confidence interval... I am not sure how to proceed... Please Help Me Thanking You Ayanendu Sanyal -- Please have a look at our new mission and contribute into it (cut and paste the

Hola, estoy intentando hacer una regresión logística entre la primera columna de mi data.table (In.hospital_death) y otras dos (GSV y BUN) , me da el error de abajo, he intentado eliminar las filas con valor NA por si esta función no lo admite, pero sigue dando el mismo error. ¿Alguien sabe porqué ocurre? (probé previamente a usar la función glm pero obtenía out of memory) library(XLConnect)

Hi the list, I am writing a package in S4 and I do not manage to understand a bug. The "R CMD check" and the "R CMD build" both work. Here is links to the package (not on CRAN yet for the raison that I explain bellow): http://christophe.genolini.free.fr/aTelecharger/kml_0.5.zip http://christophe.genolini.free.fr/aTelecharger/kml_0.5.tar.gz Then I install the package and I

Dear R users, I am having some problems with glm. The first is an error message "subscript out of bounds". The second is the fact that reasonable starting values are not accepted by the function. To be more specific, here is an example: > success <- c(13,12,11,14,14,11,13,11,12) > failure <- c(0,0,0,0,0,0,0,2,2) > predictor <- c(0,80*5^(0:7)) >

I have a quick question, and I apologize in advance if, in asking, I expose my woeful ignorance of R and its packages. I am trying to use the bootcov function to estimate the standard errors for some regression quantiles using a cluster bootstrap. However, it seems that bootcov passes arguments that rq.fit doesn't like, preventing the command from executing. Here is an example:

Dear Lister, I am facing a strange problem fitting a GLM of the negative binomial family. Actually, I tried to estimate theta (the scale parameter) through glm.nb from MASS and could get convergence only relaxing the convergence tolerance to 1e-3. With warning messages: glm1<-glm.nb(nbcas~.,data=zonesdb4,control=glm.control(epsilon = 1e-3)) There were 25 warnings (use warnings() to see

Buenos dias R-help-es, Estoy interesado en estimar una curva dosis-respuesta para un conjunto de datos y para ello, estoy utilizando la libreria "drm". Hasta ahi todo bien. Me gustaria automatizar algunas cosas y el primer paso para ello es la estimacion del modelo. Si la estimacion funciona, todo lo demas funciona; de lo contrario, todo fallara. Tengo algunas lineas que mitigan un

I've only been using R on and off for 9 months and started using the glasso package for sparse covariance estimation. I know the concept is to shrink some of the elements of the covariance matrix to zero. However, say I have a dataset that I know has some underlying "baseline" covariance/correlation (say, a value of 0.3), how can I change or incorporate that into to the

Full_Name: Ben Cooper Version: 1.5.0 OS: linux Submission from: (NULL) (134.174.187.90) The help page for glm.nb (in MASS package) says that it takes "Any other arguments for the glm() function except family" One such argument is start "starting values for the parameters in the linear predictor." However, when called with starting values glm.nb returns: Error in

If tbl is an object of class 'dist', you can do this: a <- sammon(tbl, k=3) But you can't do this: b <- isoMDS(tbl, k=3) Wouldn't it be sensible to have identical interfaces to sammon() and isoMDS() ? I think all that would be needed is to change this: isoMDS <- function(d, y=cmdscale(d, 2), maxit=50, trace=TRUE) { ...into this: isoMDS <-

Hello all, Ive been playing with nnet (package 'nnet') and Ive come across this problem. nnet doesnt seems to like to have more than 1000 weights. If I do: > data(iris) > names(iris)[5] <- "species" > net <- nnet(species ~ ., data=iris, size=124, maxit=10) # weights: 995 initial value 309.342009 iter 10 value 21.668435 final value 21.668435 stopped after 10