similar to: different random effects for each level of a factor in lme

I recently reviewed a paper that used Wald's Z in a mixed model analysis. The dataset (see end of message for complete dataset) has individual animals (SAMPLE) from within social groups, from within geographic regions. Y is the response variable. The paper ran a mixed model with year and region as fixed effects, group a random effect nested within region, and sample nested within group. I

Dear R-gurus, I have an interpretation problem regarding lme models. I am currently working on dog locomotion, particularly on some variation factors. I try to figure out which limb out of 2 generated more dispersed data. I record a value called Peak, around 20 times for each limb with a record. I repeat the records during a single day, and on several days. I tried to build two models, one

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:

Hi everybody. We have a samba4 domain deployed across serveral countries. Some of them (overseas) have a poor VPN connection with mainland. Since Samba4 does not support (yet) subtrees, we have deployed a DC in each location for domain validation. However users in mainland logon randomly at overseas location and sometimes this is a problem due to low bandwidth available. Is there any way to

Hi Ralph, > there is someone on the centos-docs mailing list who also wants to (at least) > offer a translation of the Release Notes into chinese (simplified in this > case). As those are subtleties(?) which are completely wasted on a eurocentric > guy like me. > I don't think this guy is making any sense because there's simply no difficult for all Chinese people to

In the World map, mainland "Equatorial Guinea" seems to be missing although two offshore parts, "Equatorial Guinea:Annobon" and "Equatorial Guinea:Fernando Po", are present. I'd like to be able to fill the mainland piece. How can I correct this? Thanks. -- View this message in context: http://n4.nabble.com/Mainland-Equatorial-Guinea-missing-tp1573691p1573691.html

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.

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

Scotland is the second feature in the UK data, so get it and split this one MULTIPOLYGON feature into individual POLYGONS scot = st_cast(the_uk$geometry[2],"POLYGON") # which is the largest polygon? which.max(st_area(scot)) [1] 1 # the first one. ok... plot(scot[[1]]) # mainland # add the rest of the islands for context, in grey, maybe to show they're outside our study area:

Hi out there, I am trying to fit a species accumulation curve (increase in number of species known vs. sampling effort) for multiple regions and several bootstrap samples. The bootstrap samples represent different arrangements of the actual sample sequence. I fitted a series of nlme-models and everything seems OK, but since the observations are correlated I tried to include some correlation

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

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

Hello all- I am doing a survival analysis for two species of invasive plants I outplanted to edges and interiors of island and mainland sites in a local reservoir. I am using the KM estimate and had no problem doing survdiff for my data using the following code: S4<-Surv(outplant$SurvTime, outplant$StatusD6) diff4=survdiff(S4 ~ outplant$Species+outplant$SiteType+outplant$EdgInt) diff4

Hello Jean-Marc: On 08/06/07, Jean-Marc Valin <jean-marc.valin@usherbrooke.ca> wrote: > > Either one. The question is: If we treat the software like a black > > box, and we feed in PCM audio, we get Speex encoded data out. Where is > > the information that indicates whether the encoded data contains > > speech or not? The API has a "get VAD status", but it

Yes, there is a kernel-ntfs rpm module... you can find it at... http://sorceforge.net/linux-ntfs/ S at l2, -- GNU/Linux Proud User #313158 Ixen Rodr?guez P?rez Administrador de Red Nodo CANARREOS EHT Isla de la Juventud FORMATUR. Cuba. Tel?fono: 53(046)322039 Fax: 53(046)324924 E-mail: SysAdmin at ehtij.co.cu ixenrp at ehtij.co.cu URL:http://www.ehtij.co.cu/ -- Este mensaje ha

Dear R gurus, I am trying to work out the problem given in Nested design - Montgomery - Design of Experiments p.561 I have attached a pdf of the data as well the anova table. It is a mixed model with Supplier as fixed effect and batches within the supplier as random effects. I am able to work out the error stratums as below using aov. Which agrees perfectly with the book example

Hello all: I am interested in using Speex for an application that streams audio from a (noisy) source, so I am interested in VAD and DTX operation. However, after browsing the archives of this list, I note that a number of people have not been satisfied with the operation of the VAD algorithm in Speex. This leads me to a few questions: - Is there a reference somewhere (other than the source

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

La Escuela de Estadística de las Administraciones Publicas organiza un curso de "R Fundamentos": http://www.ine.es/ine/eeaapp/sem1cur13.pdf Que el INE empiece a aproximarse hacia el mundo del software libre es una gran noticia. -- +-------------------------------------------------------------- | Francisco J. Viciana Fernández | Coordinador del Registro de Población | Servicio de

Hello Jean-Marc et al: On 07/06/07, Jean-Marc Valin <jean-marc.valin@usherbrooke.ca> wrote: > > - Is there a reference somewhere (other than the source itself) that > > explains how the latest VAD algorithm works? > > Read the source, Luke :-) (sorry) Okay. I had to ask :-) > > > - Is it possible to obtain the VAD status of a Speex stream > >