similar to: Help with na.action option

Dear Catarina, I prefer to leave the AIC value as NA for the tweedie GLM family because it takes extra time to compute and is only occasionally wanted. It's easy to compute the AIC yourself using the dtweedie() function of the tweedie package. Best wishes Gordon At 03:05 AM 14/08/2007, Catarina Miranda wrote: >Dear Gordon; > >I have also sent this email to R help mailing list,

Maybe a useful addition to the predict functions would be to return the values of the predictor variables. It just (unless there are problems) requires an extra line. I have inserted an example below. "predict.glm" <- function (object, newdata = NULL, type = c("link", "response", "terms"), se.fit = FALSE,

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)) >

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

The following is evidence of what is surely an undesirable feature. The issue is the handling, in calls to model.frame(), of an explanatory variable that has been derived as an unclassed factor. (Ross Darnell drew this to my attention.) ## Data are slightly modified from p.191 of MASS > worms <- data.frame(sex=gl(2,6), Dose=factor(rep(2^(0:5),2)), +

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) +

Hi there, I have been having trouble running negative binomial regression (glm.nb) using library MASS in R v2.15.0 on Mac OSX. I am running multiple models on the variables influencing the group size of damselfish in coral reefs (count data). For total group size and two of my species, glm.nb is working great to deal with overdispersion in my count data. For two of my species, I am getting a

Hello, I have a question regarding estimation of the dispersion parameter (theta) for generalized linear models with the negative binomial error structure. As I understand, there are two main methods to fit glm's using the nb error structure in R: glm.nb() or glm() with the negative.binomial(theta) family. Both functions are implemented through the MASS library. Fitting the model using these

Dear All, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

Using R (version 0.63.3) for MS windows, I try the following command > shell(paste("cd ",getenv("RHOME"),sep="")) which replies with an error message Too many parameters - FILES\RW0633 which appears to suggest that the space if the path name is causing difficulties to the cd command. getenv returns > getenv("RHOME") RHOME

Dear all, I would like to get glm() return its results (at least the deviance) for some given parameter values (ie without actually fitting the model). I tried to set `maxit = 0' but this does not work, eg: > glm(y ~ x, start = c(1, 1), maxit = 0) Error in glm.control(...) : maximum number of iterations must be > 0 Any idea? Thanks in advance. Emmanuel Paradis

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.

When I ran a Gamma regression in SAS, the algorithm converged. When I ran it in R, it keeps uncoverged even if I used 10000 iterations. What was wrong? I used the following code in R: glm(y ~ x1 x2 x3, control=glm.control(maxit=10000), data, family=Gamma(link="log")) [[alternative HTML version deleted]]

I have tried glm.nb in the MASS package, but many models (I have 250 models with different combinations of predictors for fish counts data) either fail to converge or even diverge. I'm attempting to use the negbin function in the AOD package, but am unsure what to use for the "random" term, which is supposed to provide a right hand formula for the overdispersion parameter.

It really is unclear what is claimed to be a bug here. But see https://stat.ethz.ch/pipermail/r-devel/2007-May/045592.html for why the bug is not in R: your old and new data do not match. Your fit is to a category. [The problem with the web interface to R-bugs was reported last week: it is being worked on.] On Mon, 30 Apr 2007, r.darnell at uq.edu.au wrote: > This is a multi-part

The FAQ says in [5.2 How can add-on packages be installed?] to install a package to a private tree, use [$ R INSTALL -l lib pkgdir_1 ... pkgdir_n] where lib gives the path to the library tree to install to. which, for me, returns Package '-l' does not exist. Has there been a change? Thank you Ross -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. |Ross

> From: Ross Darnell > > Liaw, Andy wrote: > >>From: Liaw, Andy > >> > >> > >>>From: Ross Darnell > >>> > >>>A good point but what is the value of storing a large set of > >>>predicted > >>>values when the values of the explanatory variables are lost > >>>(predicted >

Hello R-users. I haven't use R for a life time and this might be trivial - I hope you do not mind. I have a questions about arguments in the Glm-function. There seems to be something that I cannot cope. The basics are ok: > y <- as.double(rnorm(20) > .5) > logit.model <- glm(y ~ rnorm(20), family=binomial(link=logit), trace = TRUE) Deviance = 28.34255 Iterations - 1

I have a data with binary response variable, repcnd (pregnant or not) and one predictor continuous variable, svl (body size) as shown below. I did Hosmer-Lemeshow test as a goodness of fit (as suggested by a kind “R-helper” previously). To test whether the predictor (svl, or body size) has significant effect on predicting whether or not a female snake is pregnant, I used the differences between

Hello, I''ve spent a lot of time during the past month trying to get negative multinomial regression models for clustered event counts as described in (Guang Guo. 1996. "Negative Multinomial Regression Models For Clustered Event Counts." Sociological Methodology 26: 113-132., abstract at http://depts.washington.edu/socmeth2/4abst96.htm) implemented in R. A FORTRAN version of the