similar to: Error when trying to update cpglm model

Dear list, When running the script below, the optim function returns the message 'Error in function (par) : could not find function "fn"'. I've re-read the code a few times and just can't figure out why it's happening. Any help/suggestion is appreciated. Regards, Rubem ## R script library(tweedie) period<-factor(1:4) herd<-factor(1:10)

I am trying to write a wrapper for the last example in help(cv.glm) that deals with leave-one-out-cross-validation (LOOCV) for a logistic model. This wrapper will be used as part of a bigger program. Here is my wrapper funtion : logistic.LOOCV.err <- function( formu=NULL, data=NULL ){ cost.fn <- function(cl, pred) mean( abs(cl-pred) > 0.5 ) glmfit <- glm(

Dear all, I would like to Boostrap the stepAIC() procedure from package MASS for variety of model objects, i.e., fn <- function(object, data, B = 2){ n <- nrow(data) res <- vector(mode = "list", length = B) index <- sample(n, n * B, replace = TRUE) dim(index) <- c(n, B) for (i in 1:B) { up.obj <- update(object, data = data[index[, i], ])

I am fitting a logistic model to binary data. The response variable is a factor (0 or 1) and all predictors are continuous variables. The main predictor is LT (I expect a logistic relation between LT and the probability of being mature) and the other are variables I expect to modify this relation. I want to test if all predictors contribute significantly for the fit or not I fit the full

Hello all, I have count data with about 36% of observations being zeros. I found in some of the examples of the r-help mail archives that a tweedie family of distributions could be used to fit a model with random effects. Upon installing the tweedie package and attempting to fit the following model: lmer(SUS ~ 1 + (1|

Hi all I've been getting some emails from the R package maintainers that I need to update some code in a CRAN packge that uses FORTRAN, to comply with (not so recent) changes. I've been a little busy... I'm having trouble adjusting my code. I hope someone can help. The package was working fine, and a few R functions in my package had lines like this: tmp <- .Fortran(

Hello All, I have a question about glm in R. I would like to fit a model with glm function, I have a vector y (size n) which is my response variable and I have matrix X which is by size (n*f) where f is the number of features or columns. I have about 80 features, and when I fit a model using the following formula,? glmfit = glm(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13

Dear developers, I have jutst started working with caret and all the nice features it offers. But I just encountered a problem: I am working with a dataset that include 4 predictor variables in Descr and a two-category outcome in Categ (codified as a factor). Everything was working fine I got the results, confussion matrix etc. BUT for obtaining the AUC and predicted probabilities I had to add

Dear R users, I'm performing some GLMMs analysis with a negative binomial link. I already performed such analysis some months ago with the lmer() function but when I tried it today I encountered this problem: Erreur dans famType(glmFit$family) : unknown GLM family: 'Negative Binomial' Does anyone know if the negative binomial family has been removed from this function? I really

Dear R users, I’m performing some GLMMs analysis with a negative binomial link. I already performed such analysis some months ago with the lmer() function but when I tried it today I encountered this problem: *Erreur dans famType(glmFit$family) : unknown GLM family: ‘Negative Binomial’* Does anyone know if the negative binomial family has been removed from this function? I really appreciate any

Dear listers, One of my former students is trying to fit a model of the negative binomial family with lmer. In the past (two years ago), the following call was working well: m1a<-lmer(mapos~ninter+saison+milieu*zone+(1|code),family=neg.bin(0.451),REML=TRUE,data=manu) But now (R version 2.9.2 and lme4 version 0.999375-32), that gives (even with the library MASS loaded):

Hi all, I've now got a problem with some modified lmer code (function lmer1 pasted at end) - I've made only three changes to the lmer code (marked), and I'm not really looking for comments on this function, but would like to know why execution of the following commands that use it almost invariably (but not quite predictably) leads to the R session terminating. Here's the command

/Hello. SUMMARY: $ BOOM=$'\xFF' LC_ALL=en_US.UTF-8 Rscript --vanilla -e "Sys.getenv()" Error in substring(x, m + 1L) : invalid multibyte string at '<ff>' $ BOOM=$'\xFF' LC_ALL=en_US.UTF-8 Rscript --vanilla -e "Sys.getenv('BOOM')" [1] "\xff" BACKGROUND: I launch R through an Son of Grid Engine (SGE) scheduler, where the R

Dear R helpers, I need to estimate a probit model with box constraints placed on several of the model parameters. I have the following two questions: 1) How are the standard errors calclulated in glm (family=binomial(link="probit")? I ran a typical probit model using the glm probit link and the nlminb function with my own coding of the loglikehood, separately. As nlminb does not

Hello, I used the glm function in R to fit a dose-response relationship and then have been using dose.p to calculate the LC50, however I would like to calculate the NOEL (no observed effect level), ie the lowest dose above which responses start occurring. Does anyone know how to do this? [[alternative HTML version deleted]]

Hi when I fit a glm by glm.fit(x,y,family = binomial()) and then try to use the object for prediction of newdata by: predict.glm(object, newdata) I get the error: Error in terms.default(object) : no terms component I know I can use glm() and a formula, but for my case I prefer glm.fit(x,y)... thanks for a hint christoph $platform [1] "i686-pc-linux-gnu" $arch [1]

I have a logistic model fitted with the following R function: glmfit<-glm(formula, data, family=binomial) A reasonable cutoff value in order to get a good data classification (or confusion matrix) with the fitted model is 0.2 instead of the mostly used 0.5. And I want to use the `cv.glm` function with the fitted model: cv.glm(data, glmfit, cost, K) Since the response in the fitted

Tomas, I think you're not addressing the actual issue which is a clear regression in Sys.getenv() [because it used to work and still works for single env var, but not a list] and the cryptic error due to that regression (caused by changes in R-devel). So in either case, Sys.getenv needs fixing (i.e., this should really go to the bugzilla). Its behavior is currently inconsistent. The quoted

Below. -- Bert On Tue, Apr 3, 2012 at 1:47 PM, Danielle Duncan <dlduncan2 at alaska.edu> wrote: > Thanks for the response, I should have clarified that the NOEL is the > smallest dose above which there is a statistically significant effect. > This is not a scientifically meaningful nor defensible definition as it is stochastic, depends on the test used, design, level chosen, etc.

I am having a couple of problems someone may be able to cast some light on. Question 1: I am making a logistic model but when i do this: glm.model = glm(as.factor(form$finished) ~ ., family=binomial, data=form[1:150000,]) I get this: Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ (found for 'barrier') which is