similar to: Error fitting overdispersed logistic regression: package dispmod

Hi all, I used bwplot in lattice to create a 6-panel boxplot grouped by a conditioning variable (param) that displays concentration (conc) in response to treatment (trtmnt). Here is the functional part of my code followed by my three questions: library(lattice); ww<-read.csv(file="c:/Rdata/lattice_boxplot_prep.csv",header=TRUE,sep=","); attach(ww);

Hello all, I am trying to incorporate a y-axis break (i.e. the lightning bolt style) in an xyplot (using panel.superpose). I have tried to do this manually using panel.line, but panel.line will not draw to the left of the y-axis (or on the y-axis for that matter). I would prefer not to have to do this by hand, but I feel like I am running out of options. The figure can be viewed here

Hi, I'm analysing a dataset in which the same 5 subjects (male.pair) were subjected to two treatments (treatment) and were measured for 12 successive days within each treatment (layingday). Overall 5*2*12=120 observations. I want to test the effect of treatment, time (layingday) and their interaction. I have done so through the ANOVA below: >

Hi All Please see the Rprofile file which i have modified as follows and after that when I start R then I see that R says to me "TRUE" for all the packages implying that all loaded at once. But when i try to use commands as simple as help("lm"), it doesnt work nor any of the menu "Packages" is not working. Although the regression using lm ( Y ~ X ) is working

Hi, there: I have a variable whose distribution is far from normal and its qqnorm is S-shape, like a logisitic plot. My purpose is to discretize it into 2 or 3 classes. (basically, a transformation from quantative to discrete). I am wondering if there is a good way to do that. thanks, Ed

Greetings, I am using both the lrm() {Design} and glm( , family=binomial()) to perform a a logisitic regression in R. Apart from the typical summary() methods, what other methods of diagnosing logistic regression models does R provide? i.e. plotting an 'lm' object, etc. Secondly, is there any facility to calculate the R^{2)_{L} as suggested by Menard in "Applied Logistic

Hi, I am performing glm with binomial family and my data show slight overdispersion (HF<1.5). Nevertheless, in order to take into account for this heterogeneity though weak, I use F-test rather than Chi-square (Krackow & Tkadlec, 2001). But surprisingly, outputs of this two tests are exactly similar. What is the reason and how can I scale the output by overdispersion ?? Thank you,

Hi all, I was recently writing a script to identify the value and id of the maximum observation in a sliding window when I ran into some unexpected behavior. I have included an example. > test <- c() > test$elev <- c(1:200) > test$i <- 1 > test$window <- 10 The following works for me: > check.max <- function(x){obs.max <- x$elev[x$i:x$i+x$window]; obs.max}

[corrected dataset below] Hello everyone, I am trying to do within subjects repeated measures anova followed by the test of sphericity (sample dataset below). I am able to get either mixed model or linear model anova and TukeyHSD, but have no luck with Repeated-Measures Assuming Sphericity or Separate Sphericity Tests. I am trying to follow example from "car" package, but it seems

Dear R-users, Earlier this year I posted a message to this list regarding negative binomial mixed models in R. It was suggested that the program I had written should be turned into an R-package. This has now been done, in collaboration with David Fournier and Anders Nielsen. The R-package glmmADMB provides the following GLMM framework: - Negative binomial or Poisson responses. - Zero-inflation

I am wondering if stepAIC in the MASS library may be used for model selection in an overdispersed Poisson situation. What I thought of doing was to get an estimate of the overdispersion parameter phi from fitting a model with all or most of the available predictors (we have a large number of observations so this should not be problematical) and then use stepAIC with scale = phi. Should this

Information on package 'aod' Description: Package: aod Version: 1.1-2 Date: 2005-06-08 Title: Analysis of Overdispersed Data Author: Matthieu Lesnoff <matthieu.lesnoff at cirad.fr> and Renaud Lancelot <renaud.lancelot at cirad.fr> Maintainer: Renaud Lancelot <renaud.lancelot at cirad.fr> Depends: R (>=

Information on package 'aod' Description: Package: aod Version: 1.1-2 Date: 2005-06-08 Title: Analysis of Overdispersed Data Author: Matthieu Lesnoff <matthieu.lesnoff at cirad.fr> and Renaud Lancelot <renaud.lancelot at cirad.fr> Maintainer: Renaud Lancelot <renaud.lancelot at cirad.fr> Depends: R (>=

Dear all, I am fitting an nonlinear glm() using optim() by first minimising glm(resp~ var1 + var2, family=binomial, data=data)$deviance where var1= exp(-a1*dist1), and var2= exp(-a2*dist2), where a1 and a2 are parameters and dist1 and dist2 are independent variables. Next, I calculate the value of var1 (and var2) by plugging in the value of al1 (and al2) that minimises deviance, and fit

Hi all, I have the following data: rep1_treat rep2_treat rep1_control rep2_control 2 3 4 5 100 20 98 54 0 1 2 3 23 32 27

Dear R users I have been looking for functions that can deal with overdispersed poisson models. According to actuarial literature (England & Verall, Stochastic Claims Reserving in General Insurance , Institute of Actiuaries 2002) this can be handled through the use of quasi likelihoods instead of normal likelihoods. However, we see them frequently in this type of data, and we would like to

dear list! i am running an anlysis on proportion data using binomial (quasibinomial family) error structure. My data comprises of two continuous vars, body size and range size, as well as of feeding guild, nest placement, nest type and foragig strata as factors. I hope to model with these variables the preference of primary forests (#successes) by certain bird species. My code therefore looks

I would like to try a simple parametric bootstrap, but unfortunately (stupidly?) my models are "overdispersed" gams & glms. I'm hoping for a function that generates overdispersed poisson or negative binomial data with a given mean, scale (& shape parameter). The loose definition I'm using is overdispersed poisson produces integer values with variance=const*mean &

Hi, I have to analyse the number of provisioning trips to nestlings according to a number of biological and environmental factors. I was thinking of building a mixed-effects model with species and nestid as random effects, using a Poisson distribution, but the data are overdispersed (variance/mean = 5). I then thought of using a mixed-effects model with negative binomial distribution, but I have

Dear R users I have been looking for functions that can deal with overdispersed poisson models. Some (one) of the observations are negative. According to actuarial literature (England & Verall, Stochastic Claims Reserving in General Insurance , Institute of Actiuaries 2002) this can be handled through the use of quasi likelihoods instead of normal likelihoods. The presence of negatives is not