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I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA<-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family="binomial") I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 >From looking at previous help

Hi, We have data of two groups of subjects: 32 elderly, 14 young adults. for each subject we have 15 observations, each observation consisting of a reaction-time measure (RT) and an activation maesure (betadlpcv). since we want to analyze the influence of (age-)group and RT on the activation, we call: lme(betadlpcv ~ RT*group, data=our.data, random=~ RT |subject) this yields: Error in

Dear all, in the package its the print() method does not seem to correctly work in all circumstances: > selectMethod(print, "its") Method Definition: function (x, ...) { print(x@.Data <mailto:x@.Data> , ...) } <environment: namespace:its> Signatures: x target "its" defined "its" > fundPME.lst[[1]]$irr An object of

Hello everyone: It's the first time I write to this mailing list. Sorry in advance if my doubt has already been posted before, but I have been checking the archives and I haven't been able to find anything satisfactory. I am running a mixed effects model with nested effects (site and pair, referred to barn swallow nests located in different places in different farms). My dependent

Dear all, In lme, models in which a factor is fully "contained" in another lead to an error. This is not the case when using lm/aov. I understand that these factors are aliased, but believe that such models make sense when the factors are fitted sequentially. For example, I sometimes fit a factor first as linear term (continuous variable with discrete levels, e.g. 1,2,4,6), and

I have the following code: ----------------------------------------------------------------------------------------------------- result <- matrix(NA, nrow=1, ncol=5) for(i in 1:(nsnp-1)) { for(j in (i+1):nsnp){ tempsnp1 <- data.lme[,i] tempsnp2 <- data.lme[,j] fm1 <- lme(trait~sex+age+rmtemp.b+fc+tempsnp1+tempsnp2+tempsnp1*tempsnp2, random=~1|famid, na.action=na.omit) fm2 <-

While using DTrace to track down a problem recently, we came across an unexpected restriction: the compiler will not permit different speculations to be committed in the same instance of a probe identifier. For instance, consider the following useless D script: #!/usr/sbin/dtrace -s #pragma D option nspec=2 BEGIN { spec1 = speculation(); spec2 = speculation(); } END {

Dear R-ers, I am having trouble understanding why I am getting an error using glmmPQL (library MASS). I am getting the following error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 The long story: I have data from an experiment on pairwise comparisons between 3 treatments (a, b, c). So a typical run of an experiment

Using OpenSolaris 2008.11 build 110. Created vnic''s and etherstub''s and they are displayed using dladm show-links but when the server is rebooted a ''dladm show-link'' is empty although the vnic and etherstub devices are still there and function properly. That is a real inconvenience, so why do the no longer show after a reboot or shutdown/start? Is there a

Hi all, I'm having some difficulty with lme. I am currently trying to run the following simple model anova(lme(x ~ f1 + f2 + f1:f2, data=m, random=~1|r1)) Which is currently producing the error Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 x is a numeric vector containing 194 observations. f1 is a factor vector containing two levels, and

Dear R-help, Why can't lme cope with an incomplete whole plot when analysing a split-plot experiment? For example: R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) > library(nlme) > attach(Oats) > nitro <- ordered(nitro) > fit <- lme(yield ~ Variety*nitro, random=~1|Block/Variety) > anova(fit) numDF denDF F-value

(Bcc''ed to networking discuss). Hi All, Have enclosed man page draft for dlstat(1M) herewith. This is part of the effort to gain better visibility into network traffic in light of crossbow features like virtual NICs, interrupt vs. polling modes etc. This in turn would greatly assist network performance analysis. It is also aimed at segregating link/flow configuration from

Hi, I am running Fedora 7 VM. On an earlier configuration with zaptel and Asterisk 1.4.21 , meetme worked alright. I upgraded to Dahdi and Asterisk 1.4.26, and the result is choppy sound via Meeme, while a simple Musiconhold works OK with descent audio quality. So I am sure its a Dahdi_dummy problem. Running dahdi_test gave me very poor results. Opened pseudo dahdi interface, measuring accuracy... 4.727% 4.725% 4.741% 4.903% 4.715% 4.459% 4.705% Doing a Top yields the folliwng results t...

Hi R-experts, I have a problem using nlme. I use the following code to group my data: Parameterg <- groupedData( result ~ time | Batch, data = Batchdata, labels = list( x = "Time", y = "analysis") ) and then uses the nlme function to fit a nonlinear mixed model that includes Process as a fixed covariate: nlme.model001epr <- nlme(result ~ A0 * exp(- ( exp(A1)

Hi R-masters I read the article: Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. In this paper i proposed a bivariate mixed model and use SAS proc mixed to adjust the estimates. I thinks use R to make the same and try with this code: base<-read.csv("base.csv") adj<-.5 attach(base) sens<-(VP+adj)/(VP+FN+2*adj)

I decided to take a stab at 6601421 dladm set-linkprop should support multiple -p options along with some related linkprop fixes, but when I looked into dladm.c, was surprised to find that "dladm_parse_props()" is called from do_create_aggr and do_create_vlan in addition to the expected (by me, at least) do_create_vnic *linkprop functions. What happened here? Why do we pass in

Dear lme and lmer -ers, I have some problems using "home-made" contrast matrix in lme and lmer. I did an experiment to investigate the relationship between the response of an animal and some factors, namely the light wavelength (WA), the light intensity to which this animal was exposed and the sex of the animal tested. - The response can be a variable LA (normal distribution) or

Dear R users, I have a problem with something called "NaNs" in a nested mixed model. The background is that I have studied the number of insect nymphs emerging from replicated Willow genotypes in the field. I have 15 replicates each of 4 Willow genotypes belonging two 2 Willow species. Now I want to elucidate the effect of Willow genotype on the number of emerging nymphs. Previously I

Dear R users, I have a data structure as follows: id two res1 res2 c1 c2 inter 1 -0.786093166 1 0 1 2 6 3 -0.308495749 1 0 0 1 2 5 -0.738033048 1 0 0 0 1 7 -0.52176252 1 0

Author: meem Repository: /hg/zfs-crypto/gate Revision: 4d618a4110ff44c57939c29a3a8a198be2ec8352 Log message: 6336683 address::queue -v blows up kmdb and mdb 6338969 netstrategy needs to use SIOCGLIFFLAGS and friends to see IFF_VIRTUAL 6339847 ip_rcm incorrectly parses interface names 6340643 ip_rcm is lugging around dead code 6340645 ip_rcm tracks needless phyint state Files: update: