similar to: Appropriate test for overdispersion in binomial data

Hi everyone I want help in graduating the attached rates and checking for goodness of fit and smoothness using R please help. Many thnk TOo every one around the world This message and attachments are subject to a disclaimer. Please refer to www.it.up.ac.za/documentation/governance/disclaimer/ for full details. / Hierdie boodskap en aanhangsels is aan 'n vrywaringsklousule onderhewig.

Deepayan Sarkar and I have a source package of release candidate 1 of the 0.6 series of the lme4 package available at http://www.stat.wisc.edu/~bates/lme4_0.6-0-1.tar.gz This package requires Matrix_0.8-6 which has been uploaded to CRAN and should be available in a few days. A copy of the source package is available as http://www.stat.wisc.edu/~bates/Matrix_0.8-6.tar.gz

Deepayan Sarkar and I have a source package of release candidate 1 of the 0.6 series of the lme4 package available at http://www.stat.wisc.edu/~bates/lme4_0.6-0-1.tar.gz This package requires Matrix_0.8-6 which has been uploaded to CRAN and should be available in a few days. A copy of the source package is available as http://www.stat.wisc.edu/~bates/Matrix_0.8-6.tar.gz

Hi, I would be very grateful for advice on getting confidence intervals for the ordinary (non smoothed) parameter estimates from a gam. Motivation I am studying hospital outcomes in a large data set. The outcomes of interest to me are all binary variables. The one in the example here, Dead30d, is death within 30 days of admission. Sexf is gender (M or F), Age is age in years at the start

Hello, I'm attempting to use the ratetable argument to survexp in the survival package. I use the example from the ?survexp help page below, and then slightly modify it to produce an error. library(survival) data(pbc) #fit a model without any factors pfit1 <- coxph(Surv(time, status > 0) ~ trt + log(bili) + log(protime) + age + platelet, data=pbc) #this works as expected

Dear All, I get the following error when using either SASxport or Hmisc to read an .xpt file: > w <- read.xport("D:/consult/Trophos/dnp/base/TRO_ds_20081006.xpt") Erreur dans factor(x, f$value, f$label) : invalid labels; length 15 should be 1 or 14 > z<- sasxport.get("D:/consult/Trophos/dnp/base/TRO_ds_20081006.xpt") Erreur dans factor(x, f$value, f$label) :

Hi, To be able to match cases with a benchmark I need to have a data.frame with a character id variable. however, I am surprised why this seems to be so hard. In fact I was unable to succeed. Here is what I tried: >test1 <-expand.grid(ID = 1:2, sex = c("male","female")) >is(test1[,2]) [1] "factor" "oldClass" >test2 <-expand.grid(ID =

I got a similar problem when I used family=quasibinomial with my data. But, the problem disappeared when I used family=binomial. I assumed that Douglas Bates et al. had amended the lmer program to detect over-dispersion, so that it is no longer necessary to specify its possible presence with family=quasi... But, I may be wrong. If you get more information about this from the great man, then would

Dear friends, As we all know, the usual model selection criteria(e.g.deviance,AIC...) in GLMs isn't very good for selecting the best model when overdispersion exist, so we need to adjust the corresponding statistic,see(Fitzmaurice,G.M. (1997) Model selection with overdispersed

Hi, there are a lot of messages dealing with overdispersion, but I couldn't find anything about how to test for overdispersion. I applied a GAM with binomial distribution on my presence/absence data, and would like to check for overdispersion. Does anyone know the command? Many thanks, Anna -- View this message in context:

Hello everybody, I have count data and with these data, I would like to build a mixed model by using the function glmer(). In a first time, I calculated the c-hat of a simple model with glm() to verify overdispersion and I found a c-hat = 18. I also verified overdispersion in the mixed model by checking the residuals of random effects via the function glmmPQL and I found a c-hat = 15. Thus,

Hi there, I am using the geepack package and wish to test if my data (family=poisson) is overdispersed so that I may adjust the "scale.value" if necessary. Is there a specific function or method I should be using? Thanks for the advice, Bruce _____________________________ Bruce Catton MSc Candidate Forest Sciences University of British Columbia Vancouver, British

Dear R users, I am trying to use "gamm" from package "mgcv" to model results from a mesocosm experiment. My model is of type M1 <- gamm(Resp ~ s(Day, k=8) + s(Day, by=C, k=8) + Flow + offset(LogVol), data=MyResp, correlation = corAR1(form= ~ Day|Mesocosm), family=poisson(link=log)) where the response variable is counts, offset by the

Dear All I recently proposed a simple modification to Wedderburn's 1974 estimate of overdispersion for count and binomial data, which is used in glm for the quasipoisson and quasibinomial families (see the reference below). Although my motivation for the modification arose from considering sparse data, it will be almost identical to Wedderburn's estimate when the data are not sparse.

Ben Bolker writes: > This looks really useful. Base R is very conservative; despite the > fact that it would be much more easily adopted in base R, I think it > is much more likely to find a home in an add-on package such as aods3 > or glm2 than in base R ... Thanks for these suggestions Ben - Simon Wood has also been in touch, and plans to put it into mgcv David Fletcher Original

Hello, The share of concurring votes (i.e. yes-yes and no-no) in total votes between a pair of voters is a function of their ideological distance (index continuous on [1,2]). I show by other means that the votes typically are highly positively correlated (with an average c=0.6). This is because voters sit together and discuss the issue before taking a vote, but also because they share common

Hello, I am analysing counts data with a mixed model using lmer procedure. I therefore use the quasipoisson distribution but I'm not sure if this is sufficient to account for overdispersion. Actually the results are not very different to what I get when specifying a poisson distribution although my data are clearly overdispersed. this my model: >model <- lmer(NB ~ T + volume +

I am looking for the correct commands to do the following things: 1. I have a binomial logistic regression model and i want to test for overdispersion. 2. If I do indeed have overdispersion i need to then run a quasi-binomial model, but I'm not sure of the command. 3. I can get the residuals of the model, but i need to then apply a shapiro wilk test to test them. Does anyone know the command

Hi all, I have made an overdispersion test for a data set and get the following result Overdispersion test Obs.Var/Theor.Var Statistic p-value poisson data 16.24267 47444.85 0 after deleting the outliers from the data set I get the following result Overdispersion test Obs.Var/Theor.Var Statistic p-value poisson data 16.27106 0 1 The

Hello, I am trying to determine LD50 and LD95 using dose.p in MASS however some of the Residual variance is larger than the degrees of freedom. Please can anyone help with any advice as to how i can correct for this? Here is the model as inputted into R y<-cbind(dead,n-dead) model<-glm(y~log(conc),binomial) summary(model) xv<-seq(min(log(conc)-1),max(log(conc)+1),0.01)