similar to: setting up LMER for repeated measures and how do I get a p value for my fixed effect, group?

I?ve been writing some code that uses libsmbclient to talk to SMB servers and I?m a little bit confused why ?smbc_stat()? sets the st_uid & st_gid to the uid & gid of the process calling it, instead of trying to map the real owner & group of the path or setting it to -1:-1 if it can?t do that... But there is probably some good reason for this? Ie, if I call something like this: >

Hi, I was hoping to get some advice regarding the testing of interactions, when one factor is modelled as a random effect... I have a model with binomial error structure where the response variable is the proportion of time spent at the main sett (animals were tracked for 28 consecutive days in each season, and were recorded either at the main sett or an outlier sett, so the response variable is

On Sat, 2011-10-29 at 15:16 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > > Ralf, et al., > > > > > > Attached is the latest version of my autovectorization patch. llvmdev > > > has been CC'd (as had been suggested to me); this e-mail contains > >

On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > Ralf, et al., > > > > Attached is the latest version of my autovectorization patch. llvmdev > > has been CC'd (as had been suggested to me); this e-mail contains > > additional benchmark results. > > > > First, these are preliminary

On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > Ralf, et al., > > Attached is the latest version of my autovectorization patch. llvmdev > has been CC'd (as had been suggested to me); this e-mail contains > additional benchmark results. > > First, these are preliminary results because I did not do the things > necessary to make them real (explicitly quiet the

Ralf, et al., Attached is the latest version of my autovectorization patch. llvmdev has been CC'd (as had been suggested to me); this e-mail contains additional benchmark results. First, these are preliminary results because I did not do the things necessary to make them real (explicitly quiet the machine, bind the processes to one cpu, etc.). But they should be good enough for discussion.

is there a way to use the actual index value for plotting zoo objects this is the way that the index is set up and a sample range of what I would like to plot 01/01/06 00:00:00 - 01/01/06 23:45:00 { library(zoo) # chron library(chron) fmt.chron <- function(x) { chron(sub(" .*", "", x), gsub(".* (.*)", "\\1:00", x)) }} x <- structure(c(15.57, 15.5,

Hi. As shown in the simplified example below, I'm having trouble allocating memory for large vectors, even though it would appear that there is more than enough memory available. That is, even with a memory limit of 1500 MB, R 2.6.1 (Win) will allocate memory for a first vector of 285 MB, but not for a second vector of the same size. Forcing garbage collection does not seem

Hi all R users, I'm trying to replicate the same results that are given in a published article after been granted the same data that the authors use. I'm having problems to determine the cointegration rank of my data set using the Johnasen's trace test. This trace test is already programmed in the package ur.ca and can be found in the function ca.jo(). After I run the ca.jo()

On Mon, May 25, 2020 at 11:36:18PM +0200, Peter Eriksson via samba wrote: > I?ve been writing some code that uses libsmbclient to talk to SMB servers > and I?m a little bit confused why ?smbc_stat()? sets the > st_uid & st_gid to the uid & gid of the process calling it, > instead of trying to map the real owner & group of the path or setting it to -1:-1 if it can?t do

Hi, I initially posted this to the general R mailing list, but Bert Gunter thought this may be a mixed model issue, so suggested me to post here. I have a dataset that has 2 groups of subjects. For each subject in each group, the response measured is the number of success (no.success) obatined with the number of trials (no.trials). So a probability of success (prop.success) can be computed as

Dear R-users, I'm somehow new to the topic of mixed models. I'd liked to simulate data with a specific correlation-structure and analyze it with lme(). I have a fixed effect (group) and a random effect (block): y group block -5.2158969 -2 1 -2.4174197 -2 1 -2.6731572 -2 1 -3.6310082 -2 1 -0.3888706 0 2 1.3422255 0 2

Hello, all R-users! I am working on fitting a survival analysis model using the coxph function for Cox proportional hazards regression model. Data look like usual: ========================== group block death censor Group1 1 4 1 Group1 1 12 1 ... Group2 30 4 1 Group2 30 4 1 ... Group3 57 16

Hi, I've been doing an experiment, measuring the dead-zone-diameters of bacteria, when they've been grown with paper diffusion disks of antimicrobial. There are two groups, or treatments - one is bacteria that have been cultured in said antimicrobial for the past year, the other group is of the same species, but lab stock and has not gone had any prior contact with the antimicrobial.

I am having difficulty understanding how I would constrain a data set by filtering out 'records' based on certain criteria. Using SQL I could query using 'select * from my.data where LithClass in ('sand', 'clay')' or some such. Using subset, there seem to be ghosts left behind (that is, all of the LithClass *.Labels* remain after subset) > dput(tcc)

Re: the smbc_stat() returning wrong st_uid/st_gid: Yeah, I get why it?s difficult, but perhaps it should just return -1/-1 instead? It too is wrong but probably less confusing than seeing the local user?s uid/gid there?? Anyway, I?ve been working on a ?grand unifying :-)? ACL managing (CLI) tool since I got tired of all the different and quite frankly confusing/frustrating tools to manage ACL

I'm working with a very large matrix ( 22k rows x 2k cols) of RNA expression data with R v.2.5.0 on a RedHat Enterprise machine, x86_64 architecture. The relevant code is below, but I call a function that takes a cluster of this data ( a list structure that contains a $rows elt which lists the rows (genes ) in the cluster by ID, but not the actual data itself ). The

Hi all, I'm trying to work out the following problem, but I can't imagine how. I have the following (much reduced & oversimplified) dataset My.df <- cbind.data.frame(PPM=c(15.78, 15.81, 15.87, 15.83, 15.81, 15.84, 15.91, 15.90, 15.83, 15.81, 15.93, 15.83, 15.70, 15.92, 15.76, 15.81, 15.91, 15.75, 15.84, 15.86, 15.82, 15.79,

Dear all, I have a lot of problems on R-programming. for example my csv. file is .. Date wrfRH wrfsolar wrfwindspeed wrfrain wrftd wrfta 21/10/2010 92.97 22.11 53.27 0 1546.337861 61.00852664 22/10/2010 87.35 21.99 40.89 0 1300.408288 62.85352227 23/10/2010 88.38 21.71 28.04 0.01 1381.768284 54.80594493 24/10/2010 92.32 15.45 22.38 0.51 1113.90981 39.46573663 25/10/2010 93.42

Hi all, Using: R version 2.8.1 Patched (2009-03-07 r48068) on OSX (10.5.6) with survival version: Version: 2.35-3 Date: 2009-02-10 I get the following using the first example in ?summary.survfit: > summary( survfit( Surv(futime, fustat)~1, data=ovarian)) Call: survfit(formula = Surv(futime, fustat) ~ 1, data = ovarian) time n.risk n.event survival