similar to: make check-all fails (PR#8063)

Hi, I noticed this problem on my home desktop running FC4 and again on my laptop running FC5. Both have previously compiled and passed make check-all on 2.3.1 svn revisions from 10 days ago or so. On both these machines, make check-all is consistently failing (4 out of 4 attempts on the FC 4 desktop and 3 out of 3 on the FC 5 laptop) in the p-r-random-tests tests. This is with both default

Dear List, A few weeks ago a discussion took place regarding Fedora Core 4 and compiling R on that platform using the new version 4 of gcc and its gfortran compiler. gcc was recently updated to 4.0.1 on FC4 (4.0.1-5 in Red Hat Land) so I thought I'd give compiling R a go on my laptop which needed updating anyway. I've had trouble using the optimisation flags I used to use under FC3 with

I have a dual Xeon x86_64 system running Red Hat AS 4. There are no x86_64 rpms in http://cran.us.r-project.org/bin/linux/redhat/el4/ (the i386 ones are a point release behind anyway) , and the fc4 rpms have a whole web of dependencies I don't want to pull in. So I decided to build http://cran.us.r-project.org/bin/linux/redhat/SRPMS/R-2.2.1-1.fc3.src.rpm . When I ran rpmbuild. one of the

R glmmADMB question I am trying to use glmm.admb (the latest alpha version from the R forge website 0.6.4) to model my count data that is overdispersed using a negative binomial family but keep getting the following error message: Error in glmm.admb(data$total_bites_rounded ~ age_class_back, random = ~food.dif.id, : Argument "group" must be a character string specifying the

Hi there, I am new to the package glmmadmb, but need it to perform a zero-inflated gzlmm with a binomial error structure. I can't seem to get it to work without getting some strange error messages. I am trying to find out what is affecting the number of seabird calls on an array of recorders placed at 4 sites on 6 islands. I have nightly variables (weather and moonlight), site variables

Hello everybody, I am using glmmADMB and when I run some models, I recieve the following message: Erro em glmmadmb(eumencells ~ 1 + (1 | owners), data = pred3, family = "nbinom", : The function maximizer failed (couldn't find STD file) Furthermore: Lost warning messages: Command execution 'C:\Windows\system32\cmd.exe /c

library(glmmADMB) #Example for glmm.admb data(epil2) glmm.admb(y~Base*trt+Age +Visit,random=~Visit,group="subject",data=epil2,family="nbinom") Gives: Error in glmm.admb(y ~ Base * trt + Age + Visit, random = ~Visit, group = "subject", : The function maximizer failed ****************** R version 2.4.1 RC (2006-12-14 r40181) powerpc-apple-darwin8.8.0 locale: C

Hello all, I would like to fit a mixed effects model, but my response is of the negative binomial (or overdispersed poisson) family. The only (?) package that looks like it can do this is glmm.ADMB (but it cannot run on Mac OS X - please correct me if I am wrong!) [1] I think that glmmML {glmmML}, lmer {Matrix}, and glmmPQL {MASS} do not provide this "family" (i.e. nbinom, or

[cc'ed back to r-help] I've started to take a look, and there's nothing immediately obvious about the problem with the fit (the warnings and errors are about a "non-positive-definite Hessian", which usually means an overfitted/poorly identified model) -- still working on whether there's a way to get more useful information. As it turns out, glmmADMB's default

Dear R-help, We've been trying, so far unsuccessfully, to compile R as 64-bit under AIX 5.1. Following the recent post by Dr. Christoph Pospiech, I started with R-1.8.1 and manually edited the configure script according to the .diff file. Part of the diff has: *************** *** 24446,24452 **** int main () { struct tm *tm; ! time_t ct; ctime(&ct); ct = ct - (ct

Dear all, I am having some problems trying to run a GLMM model with zero-inflation using the alpha version of glmmADMB (0.6.4) using R (2.13.1) in Windows and I would greatly appreciate some help. My count response variable (number of birds: count) fits a negative binomial distribution and the explanatory variables are both continuous and categorical (species= 17). The three random effects are

Greetings glmmADMB function users, I am trying to run a series of models using the glmmADMB function with several different distribution families (e.g., poisson, negbinom). I am using a Optiplex 790 PC with Windows 7, 16.0 GB of RAM and a 64-bit operating system. I am running R version 2.15.0 and started out using the most recent version of glmmADMB (I believe version 7.2.15). My data is zero

Hello, I like to fit data against a negative binominal distribution x2<-c(rep(10,14),rep(9,8),rep(8,13),rep(7,11),rep(6,6),rep(5,18),rep(4,7),re p(3,21),rep(2,33),rep(1,55),rep(0,225)) f2<-fitdist(x2,"nbinom",method="mle") plot(f2) summary(f2) gofstat(f2) I receive the following result: Fitting of the distribution ' nbinom ' by maximum

I''m using RSpec on Rails and would like to clean up the backtraces, so I went looking for an RSpec equivalent to ThoughtBot''s Quiet Backtrace gem<http://thoughtbot.com/projects/quietbacktrace>. I found Spec::Runner::QuietBacktraceTweaker in the RDOCS, but I can''t figure out how to use it. Can I add something to spec_helper.rb that will utilize

This is kind of a two part question. Question One: I want to be sure that an Order model is protecting sensitive attributes from mass assignment. The example looks like this: describe Order do it "should protect total attribute from mass assignment" do @order = Order.new(:total => 0.05) @order.total.should_not == 0.05 end end And the code to implement it: class Order

Hey David, I just watched the RubyConf recording of your RSpec session and noticed that you''re using a modified version of TextMate that uses a sidebar instead of a project drawer. Where did you find that mod? -------------- next part -------------- An HTML attachment was scrubbed... URL: http://rubyforge.org/pipermail/rspec-users/attachments/20071207/c900e1bc/attachment.html

Now that RSpec is nearing 1 dot oh, are there any plans to implement an integration testing equivalent in RSpec On Rails?

I just read ''Mocks Aren''t Stubs'' and was intrigued by the notion of ''outside-in'' TDD. As a Rails developer, I''m curious if others are employing this method when developing Rails applications using RSpec. Is it common practice (or even practical) to drive the development of a Rails app by starting with view specs, then controller specs, then

Sprinkling my examples with ''lambda'' has always seemed like a bit of a wart to me. I''ve gotten into the habit of adding ''alias :calling :lambda'' to my spec suites. My examples then look like: calling { Foo }.should raise_error calling { Bar }.should_not raise_error Is there a reason that RSpec core has chosen not to make exception expectations more

Dear R users, Does anyone know how to get standard errors of the parameter estimates in glmm.admb? Thanks, Istvan