similar to: lme4_0.995-2/Matrix_0.995-4 upgrade introduces error messages (change management)

I installed the MinGW-5.0.0 compilers today (gcc-3.4.2 is now contained in the 'Current' distribution. When the current compilers failed to compile the referenced Matrix update and not knowing how to check the exact version numbers for individual files, I applied the Id.exe and f771.exe fixes from the 'Building R for Windows' page (http://www.murdoch-sutherland.com/Rtools/#ldbug).

I am compiling 2.2.8 on a Caldera Openlinux box that was running 2.0.5 or some such. It already has a working config and uses samba.d dirs for it's files: /etc/samba.d /var/lock/samba.d /var/log/samba.d etc During compile I use following switches --with-piddir=/var/lock/samba.d --with-lockdir=/var/lock/samba.d However the pid files are ending up in /var/run/samba instead of

On 27-01-2024 11:56, Rowland Penny via samba wrote: > On Fri, 26 Jan 2024 22:22:49 -0500 > Mark Foley via samba<samba at lists.samba.org> wrote: > >> On Wed Jan 24 05:03:25 2024 Rowland Penny via samba >> <samba at lists.samba.org> wrote: >>> On Tue, 23 Jan 2024 17:07:35 -0500 >>> Mark Foley via samba<samba at lists.samba.org> wrote:

Signed-off-by: Ilia Mirkin <imirkin at alum.mit.edu> --- Hope I understood you correctly wrt the mem exec stuff. nvkm/subdev/fb/ramnv50.c | 2 +- nvkm/subdev/fb/ramnva3.c | 2 +- nvkm/subdev/fb/ramnvc0.c | 2 +- nvkm/subdev/fb/ramnve0.c | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/nvkm/subdev/fb/ramnv50.c b/nvkm/subdev/fb/ramnv50.c index ef91b6e..e5d12c2 100644

Dear useRs (called Frank Harrell, most likely), after having preached for years to my medical colleagues to be cautious with stepwise selection procedures, they chanted back asking for an alternative when using mixed models. There is a half dozen laXXX packages around for all types of linear models, but as far I see there is none for mixed models such as lme. Even boot.stepAIC (which I

Adds a NvReclock boolean option to allow the user to enable (or disable) reclocking. All chipsets default to off, except NVAA/NVAC, which are reportedly complete. Signed-off-by: Ilia Mirkin <imirkin at alum.mit.edu> --- Ben, I know you've been saying that reclocking is in a pretty bad state, but I do think that there are going to be groups of people for whom the current code can work

Hi, I want to compute the quantiles of Chi^2 distributions with different degrees of freedom like x<-cbind(0.005, 0.010, 0.025, 0.05, 0.1, 0.5, 0.9, 0.95, 0.975, 0.99, 0.995) df<-rbind(1:100) m<-qchisq(x,df) and hoped to get back a length(df) times length(x) matrix with the quantiles. Since this does not work, I use x<-c(0.005, 0.010, 0.025, 0.05, 0.1, 0.5, 0.9, 0.95, 0.975,

I just updated the packages on my pc (windows xp). When loading lme4 I get > library(lme4) Error: package 'Matrix' 0.995-1 was found, but >= 0.995.2 is required by 'lme4' 'Matrix' 0.995-1 is indeed installed on my computer, but update.packages() does not capture a never version; and seemingly for good reasons: When looking at CRAN, the new version of Matrix is

On Thu, 19 Apr 2018, Valeri Galtsev wrote: > On Wed, April 18, 2018 8:36 pm, Always Learning wrote: >> Hi, >> >> I have a machine with a BIOS that does not permit DVD installation. It >> accepts everything else including some old superseded media types. >> >> Is it possible to download C6 combined parts 1 and 2 not using Torrent ? >> >> I have an

Dear list, I am needing to extract the estimate of overdispersion (deviance / residual degrees of freedom or c-hat) from multiple model objects - so they can then be used to compare the extent of overdispersion among alternative models as well as calculate qausi-AIC values. I have been unable to do this, despite consulting a number of manuals and searching the R-help. I am imaging that in

Hi! Apologies if this is not the correct place to ask. I am attempting a MFA analysis of a dataset based on wine chemical and sensory analysis, based on the STHDA tutorial [1]. (I am using this dataset here too, as an example dataset to work on without posting my actual data. I've tried this with both my data and the example data, with the exact same results.) The only issue I am having is

I suspect the weights argument is not having any effect. Package: Matrix Version: 0.995-2 Date: 2006-01-19 Beginning with this: Browse[1]> resp.lmer <- lmer(SensSSC ~ Block + Season + (1 | Plot) + (1 | Ma) + (1 | Pa) + + (1 | MaPa), weights = SensSSC.N, data = xx) I group the output into a table with my ran.eff function and get this:

Hi, I've cross-posted this to freedesktop@, as the xdg@ list is only used for actual specification development. On Mon, 22 Oct 2018 at 00:36, Jacob Lifshay <programmerjake at gmail.com> wrote: > Hi, we were thinking of asking if freedesktop would host Kazan (https://github.com/kazan-3d/kazan) for us, however some of our community members have objections with how freedesktop's

Dear folks, I have a question about the image() function in R. I found the following link talking about this but the replies didn't help with my situations. http://r.789695.n4.nabble.com/question-on-image-function-td839275.html#a839276 To be simple, I will keep using the example in the above link. Suppose the data are like x y mcpvalue 0.4603578

Dear friends, My dataset is like the following: x y mcpvalue 0.4603578 0.6247629 1.001 0.4603715 0.6247788 1.001 0.4603852 0.6247948 1.001 0.4110561 0.5664841 0.995 The x and y variables are unsorted. I use the function image(x,y,mcpvalue) to generate a plot, but the error is that "increasing 'x' and

Hi, When I run the survfit function, I want to get the restricted mean value and the standard error also. I found out using the "print" function to do so, as shown below, print(km.fit,print.rmean=TRUE) Call: survfit(formula = Surv(diff, status) ~ 1, type = "kaplan-meier") records n.max n.start events *rmean *se(rmean) median 200.000

Dear all, I am analyzing some data that requires a mixed model. I have been reading Pinheiro and Bates' book, but cannot find the notation to fit the following model: Suppose I have the dataset below. Here I am fitting variable p as a fixed effect, variable h as a random effect and variable t as nested within h. I would like to include the variable j as well as an independent (non-nested)

I am running a binomial glm with response variable the no of mites of two species y->cbind(mitea,miteb) against two continuous variables (temperature and predatory mites) - see below. My model shows overdispersion as the residual deviance is 48.81 on 5 degrees of freedom. If I use quasibinomial to account for overdispersion the dispersion parameter estimate is 2501139, which seems

Dear R users, I am estimating Poisson mixed models using glmmPQL (MASS) and lmer (lme4). We know that glmmPQL do not provide the correct loglikelihood for such models (it gives the loglike of a 'pseudo' or working linear mixed model). I would like to know how the loglike is calculated by lmer. A minor question is: why do glmmPQL and lmer give different degrees-of-freedom for the same

> > Can we also challenge this "torrents are untrustworthy" attitude. > > Having, successfully so far, resisted/repelled several devious > attacks from the Russians, I am keen to maintain a clean, and thus > secure, system as possible. If you don't trust the sha256 hashes, there's no reason to trust a download using https. -- Yves Bellefeuille <yan at