similar to: accounting for overdispersion in poisson distribution with lmer procedure

Hi all, I have created forest plots using the package "meta" that I submitted as pdf for publication. I just received an email from the editor asking me if I could send these files in an Excel or MS Word format such that they can treat them as tables with box plots. I am not sure if I can do this... Is this possible? I've tried the capture.output command, but I get a blank MS Word

Dear list, After reading different mails, blogs, and tried a few different codes without any success, I am asking your help! I have the following data frame where each row represent a survey unit with the following variables: > names(RV09) [1] "record.t" "trip" "set" "month" "stratum" "NAFO" [7]

Hi, I am trying to use the R command "system" under windows (XP). If I try the simple command system("mkdir toto") to create a directory toto, it tells me that it cannot find the command mkdir... Does anybody knows how it works ? Is it a path problem ? Maybe the answer is simple: I am a R user under Unix/Linux and I don't know well R under windows. Thanks ! Aurelie

hello, we are french student, we have problem for export the test on Excel. we didn't succed for save and name the test so we can't export it... can you help us please thanks _________________________________________________________________ mobile comme sur PC ! http://mobile.live.fr/messenger/bouygues/

Hi, I am trying to use the R command "system" under windows (XP). If I try the simple command system("mkdir toto") to create a directory toto, it tells me that it cannot find the command mkdir... Does anybody knows how it works ? Is it a path problem ? Maybe the answer is simple: I am a R user under Unix/Linux and I don't know well R under windows. Thanks ! Aurelie

Dear list, I have this dataframe: > names(events) [1] "EID" "X" "Y" "trip" "tow" "catch" "effort" "depth" [9] "season" Where some of my unique ID "EID" appears more than once in 162 cases. > length(events$EID)-length(unique(events$EID)) [1] 162 I would like to combined

Hello, I have some problems to generate graph with R... I am working on two different platform : - Compaq Alpha Server (Unix True 64 5.1) + R 1.6 - Sparc Server (Sun Solaris 8) + R 1.6 I use different functions like the bitmap function, the legend function and the barplot function. The graph are made by the same script on the both platform. I obtain nice graph on the compaq server but the

Dear list, I am getting an error message with "pin" and I am not quiet sure how to fix it. library(maps) op<-par(mar=c(1,1,1,1)) map(xlim=c(-40,-45),ylim=c(60,65), boundary=T, fill=T, col='gray95') >Error in par(pin = p) : invalid value specified for graphical parameter "pin" par("pin") > par("pin") [1] 8.188889 8.188889 I saved an

Dear list, I have been struggling for some time now with this code... I have this vector of unique ID "EID" of length 821 extracted from one of my dataframe (skate). It looks like this: > head(skate$EID) [1] "896-19" "895-8" "899-1" "899-5" "899-8" "895-7" I would like to remove the complete rows in another dataframe

Dear list, I am new with ggplot2 and I have spend quiet some time putting together the following code to create the attached plot. However there's still a few things that I'm having trouble with! I would be grateful if someone can tell me how to fix (1) the colour of my bars into grey scales (2) removing the y-axis (species name) on the right figure to avoid duplication, and (3) fix the

Hi all, When running a prediction using RF on another data, I get two columns returned: row number(?) and predicted class. Is there a way of appending the unique row value from an ID column in the dataframe to the predictions instead of the row number? I'm assuming that the returned results follow the data frame in that the first result returned equals the first entry in the dataframe. i.e.,

Hi all, I am trying to set up a simple path analysis in the SEM package, but I am having some trouble. I keep getting the following error message or something similar with my model, and I'm not sure what I'm doing wrong: Error in solve.default(C) : system is computationally singular: reciprocal condition number = 2.2449e-20 In addition: Warning message: In sem.default(ram = ram, S = S,

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