similar to: Simplification of Generalised Linear mixed effects models using glmmPQL

Dear R users I have built the following model m1<-lmer(y~harn+foodn+(1|ass%in%pop%in%fam),family = "quasibinomial") where y<-cbind(alive,dead) where harn and foodn are categorical factors and the random effect is a nested term to represent experimental structure e.g. Day/Block/Replicate ass= 5 level factor, pop= 2 populations per treatment factor in each assay, 7 reps

Hi all, I try to make a split-plot with poisson errors using glmmPQL, but I have some doubts about the model simplification. Look my system: Block = 3 blocks Xvar1 = 2 levels Xvar2 = 13 levels Yvar = Count data Response I need know about the behaviour of Var1, Var2 and interaction Var1:Var2. Look the levels: > levels(Xvar1) [1] "A" "B" > levels(Xvar2) [1]

Hi, I used the commands below to make Hartley's table, but some values are NA. require(SuppDists) trat = seq(2, 15, 1) gl = seq(2, 40, 1) har = matrix(0, nr=length(gl), nc=length(trat)) for(i in 1:length(gl)) for(j in 1:length(trat)) har[i,j] <- qmaxFratio(.95, df=gl[i], k=trat[j]) rownames(har) <- gl colnames(har) <- trat head(har) The output (head): 2

Hi, I install last wine version (20011016) and have problem (or I don't understand). I'm running wine without windows. My config in $HOME/.wine/config: [Drive C] "Path" = "/usr/share/wine-c" "Type" = "hd" "Label" = "MS-DOS" "Filesystem" = "win95" [wine] ... "path" =

Hey I am just organizing the CentOS village at HAR and need a rough estimate of people who will be attending. What do people want? Shall the CentOS people have their own party tent? We are currently with the Fedora people. I will also post on the SL mailing list. Hope to see you all at HAR? Cheers Didi ---- My www page: www.ribalba.de Email / Jabber: ribalba at gmail.com Skype : ribalba

I have a couple of Samba boxes, one of which is dedicated to WINS. The other I dole out anti-virus stuff to 3,500 odd PCs from. This second one will disappear from the WINS server from time to time which is a bit of a pain. I use nmblookup -R -U <wins_ip> <2nd_box_ip> to test for it. All clients are configured as "P" nodes so WINS is a bit essential. To bring it back I

I am working with xts dependent data, and my code is as follows (the problem is explained throughout): dat <- getdat("prices") dat <- read.zoo(dat, sep = "",format="%d/%m/%Y %H:%M", tz="", FUN=NULL, regular=TRUE, header=TRUE, index.column=1, colClasses=c("character", "numeric")) dat <- as.xts(dat)

I'm a bit confused if this is possible to do. I would like to write some instructional pages using markdown, here is a short example of my problem. The text looks like this: ============================================================ Risken ?r dock att du har lite mer komplicerade objekt, tex att det ?r uppbyggt av flera delobjekt:  Om

Dear all, I am experiencing problems using the glmmPQL function in the MASS package (Venables & Ripley 2002) to model binomial data with spatial autocorrelation. My question - is the presence of birds affected by various hydrological parameters? Presence/absence data were collected from 83 sites and coupled against hydrological data from the same site. The bird survey sampling effort

here's how i mounted a shared folder: #mount -t smbfs -o username=me,password=psw,workgoupt=kppo //har-file2/new-gp /samba/k-drive and when trying to un-mount using umount /samba/k-drive or, smbumount /samba/k-drive I get this message: 'device is resource busy' Any idea why i can' unmount this? please reply to all, thanks!

On Wed, Jan 28, 2015 at 4:05 PM, Ahmed Bougacha <ahmed.bougacha at gmail.com> wrote: > Hi Chandler, > > I've been looking at the regressions Quentin mentioned, and filed a PR > for the most egregious one: http://llvm.org/bugs/show_bug.cgi?id=22377 > > As for the others, I'm working on reducing them, but for now, here are > some raw observations, in case any of

I am trying to work out what analysis to perform on two coupled time-series. I want to ask how the coupling (i.e. lag or correlation) changes over time. The time series are of 4 replicate pred-prey time series where the series appear more tightly coupled at peaks but drift apart during the decline and trough phases of the cycles. The time series are short (70-80 weeks/points replicated 4

Hello, Im trying to create a heatmap with a dataset (38 x 15037) but get the error below: Error: protect(): protection stack overflow Execution halted or Error: C stack usage is too close to the limit Execution halted I tried to increase the stack size by changing: extern uintptr_t R_CStackLimit but my systems manager said that R by default uses all the memory available to it from the

I think the code is part of the RTAQ package but is not included in it, as I obtained it from https://r-forge.r-project.org/scm/viewvc.php/pkg/RTAQ/R/HAR_model.R?view=markup&root=blotter&sortby=author&pathrev=1028. It is not my code and I make no claim to other's good work, and apologize if I should even be posting it I am not sure, but in the transform function it allows to

On Wed, Jan 28, 2015 at 4:47 PM, Chandler Carruth <chandlerc at gmail.com> wrote: > > On Wed, Jan 28, 2015 at 4:05 PM, Ahmed Bougacha <ahmed.bougacha at gmail.com> > wrote: > >> Hi Chandler, >> >> I've been looking at the regressions Quentin mentioned, and filed a PR >> for the most egregious one: http://llvm.org/bugs/show_bug.cgi?id=22377

I filed a couple more, in case they're actually different issues: - http://llvm.org/bugs/show_bug.cgi?id=22412 - http://llvm.org/bugs/show_bug.cgi?id=22413 And that's pretty much it for internal changes. I'm fine with flipping the switch; Quentin, are you? Also, just to have an idea, do you (or someone else!) plan to tackle these in the near future? -Ahmed On Thu, Jan 29, 2015 at

I may get one or two in the next month, but not more than that. Focused on the pass manager for now. If none get there first, I'll eventually circle back though, so they won't rot forever. On Jan 30, 2015 11:21 AM, "Ahmed Bougacha" <ahmed.bougacha at gmail.com> wrote: > I filed a couple more, in case they're actually different issues: > -

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

Dear Tom, Short answer, if your using spec.pgram(), use the smoothing kernel to get a better estimate at the frequency centered in the bandwidth. If your frequency bin of interest is wider than the bandwidth of the kernel, average across frequencies (I think). The estimate appears to be normalized already. If you are calculating your PSD independently, then oversample (e.g. 2, perhaps 4 or more

On Tue, Feb 17, 2015 at 10:47 PM, David Blaikie <dblaikie at gmail.com> wrote: > > > On Tue, Feb 17, 2015 at 10:27 PM, Chris Lattner <clattner at apple.com> > wrote: > >> On Feb 17, 2015, at 1:58 PM, Chandler Carruth <chandlerc at gmail.com> >> wrote: >> > I'm not actually worried about this change though Chris, at least >> w.r.t.