similar to: lme: how to compare random effects in two subsets of data

Hi All, I would like to ask a question on fixed and random effecti in lme. I am fiddlying around Mick Crawley dataset "rats" : http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/ The advantage is that most work is already done in Crawley's book (page 361 onwards) so I can check what I am doing. I am tryg to reproduce the nested analysis on page 368:

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious

Hello LLVM-dev, I’m writing some crypto math code in C, and trying to make it as simple and portable as possible, so that there won’t be any unforeseen platform bugs. Ideally, I’d like the compiler to as much work as possible for me, and I’m wondering if you know how to make clang (or gcc, for that matter) do some of this. So I have a couple of questions. If this is the wrong list to ask for

On Feb 17, 2014, at 6:38 PM, Stephen Checkoway <s at pahtak.org> wrote: > > On Feb 17, 2014, at 8:10 PM, Michael Hamburg <mike at shiftleft.org> wrote: > >> First, addition. I have multiprecision integer objects, and I’d like to add them component-wise (likewise, subtract, negate, mask…). For example: >> >> struct mp { >> int limb[8]; >> }

Dear R user, I am trying to reproduce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the

Hi Everyone, I need to keep 2 systems identical. Mostly e-mail directories, web directories, mysql, etc. The goal here is to have a 2nd system ready to go it the first one starts to exhibit hardware issues. What are options to have this happen? I'm going out on a limb and thinking rsync but I haven't used it past just simple use cases. Can anyone provide some insight for me? CentOS

Hi R users: Is there a function to obtain the correlation within groups from this very simple lme model? > modeloMx1 Linear mixed-effects model fit by REML Data: barrag Log-restricted-likelihood: -70.92739 Fixed: fza_tension ~ 1 (Intercept) 90.86667 Random effects: Formula: ~1 | molde (Intercept) Residual StdDev: 2.610052 2.412176 Number of Observations: 30 Number

Is it possible to run the CVS-HEAD/Stable version of Zaptel (1.2 whatever) with an older version of Asterisk? I'm running 1.09, but I was wondering if I could get at the newer echo cancellers like KB1 and MG2 without upgrading to Asterisk 1.2? I'm going out on a limb here to try and fix a serious echo problem on a TDM + BT PSTN line in the UK Thanks for your suggestions everyone --

I would appreciate some thoughts on using the bootstrap functions in the library "bootstrap" to estimate confidence intervals of ICC values calculated in lme. In lme, the ICC is calculated as tau/(tau+sigma-squared). So, for instance the ICC in the following example is 0.116: > tmod<-lme(CINISMO~1,random=~1|IDGRUP,data=TDAT) > VarCorr(tmod) IDGRUP = pdLogChol(1)

On Fri, Jun 11, 2010 at 3:28 PM, Bill Hart <goodwillhart at googlemail.com> wrote: > Hi Eli, > > On 11 June 2010 22:44, Eli Friedman <eli.friedman at gmail.com> wrote: >> On Fri, Jun 11, 2010 at 10:37 AM, Bill Hart <goodwillhart at googlemail.com> wrote: >>> a) What plans are there to support addition, subtraction, >>> multiplication, division,

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 just wanted to send out some information that will hopefully help others. I don't know, maybe I'm the only one that's been having problems with this. I've been pulling my hair out for a while wondering why Google would not send my incoming calls to my Asterisk box. The calls would just roll to voice mail and no packets ever reached Asterisk. This has happened on two separate

> On 11 April 2019 17:44 David Salisbury via dovecot <dovecot at dovecot.org> wrote: > > > On 4/11/2019 1:50 AM, Aki Tuomi wrote: > > > >> ... > >> So, not being an expert at encryption, what are the ramifications of > >> those digests being read as different values in the two different > >> places???? I do notice that the

Dear list, I trying to make a wind rose plot whit the command oz.windrose, from plotrix package. My data, a matrix of percentages with the rows representing speed ranges and the columns indicating wind directions was generated using bin.wind.records command from same package: [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.4405286 0.0000000 0.1468429 0.4405286 0.4405286

I have an interesting lme - problem. The data is part of the Master Thesis of my friend, and she had some problems analysing this data, until one of her Jurors proposed to use linear mixed-effect models. I'm trying to help her since she has no experience with R. I'm very used to R but have very few experience with lme. The group calls of one species of parrot were recorded at many

hello, I have some data that looks similar to this (only not as nice as this): Y <- c(abs(rnorm(100, 0.10, .1)), seq(.10, 1.0, .3)+rnorm(1, 0, .5) , seq(0.8, 4.0, .31)+rnorm(1, 0, .5) , seq(3.9, .20, -.2)+rnorm(1, 0, .5) , abs(rnorm(100, 0.13, .1)) , seq(.10, 1.2, .35)+rnorm(1, 0, .5) , seq(0.7, 6.0, .31)+rnorm(1, 0, .5) , seq(5.9, .23, -.18)+rnorm(1, 0, .5) , abs(rnorm(50, 0.18, .1)) )

Does: rainbow(3)[1] rainbow(3)[2] rainbow(3)[3] ... solve your issue? B. > On Jun 8, 2017, at 8:20 AM, WRAY NICHOLAS <nicholas.wray at ntlworld.com> wrote: > > Hi R folk I have a distance time graph for a locomotive and at various times > different events occur on board the loco. I want to put a vertical line on the > speed time graph for each event, but I

Hi R folk I have a distance time graph for a locomotive and at various times different events occur on board the loco. I want to put a vertical line on the speed time graph for each event, but I want to colour each different kind of event differently to see visually whether there's any pattern to these events happening. I could just create a vector of colours and use abline which is easy

Hi Folks, I'm trying to understand the model specification formalities for 'lme', and the documentation is leaving me a bit confused. Specifically, using the example dataset 'Orthodont' in the 'nlme' package, first I use the invocation given in the example shown by "?lme": > fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age Despite the

Dear R user, I am trying to reproduce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the