similar to: Random effects and non-linear models

Sauda??es. Como que se faz um conector RJ45 em uma ponta e RJ11 e outra. Pretendo conectar a linha de um ATA em uma placa Khomp KFXO IP. A ponta que tem o conector RJ45 est? crimpada com a sequencia 568B e vai ser conectada na placa Khomp, mas a ponta RJ11 eu n?o sei como deve ficar. Li alguns manuais na internet mas n?o entendi ao certo como tem que ser feito. -- Att.* *** Luis H. Forchesatto

Hi, I have a R Package. It is working, but I can't that it install the dependencies automatically. When I try to install I receive this message: > install.packages("./RT4Bio_1.0.tar.gz",dependencies=TRUE) Installing package(s) into '/usr/local/lib/R/site-library' (as 'lib' is unspecified) inferring 'repos = NULL' from the file name ERROR: dependency

Can GLMM take formula derived from another object? foo <- glm (OVEN ~ h + h2, poisson, dataset) # ok bar <- GLMM (OVEN ~ h + h2, poisson, dataset, random = list (yr = ~1)) #error bar <- GLMM (foo$formula, poisson, dataset, random = list (yr = ~1)) #Error in foo$("formula" + yr + 1) : invalid subscript type I am using R2.1.0, lme4 0.8-2, windows xp. Below is a dataset if you

Hello all, In a previous posting regarding glmm.admb it is stated that glmm.admb can handle 2 nested random effects. I can only fit a single random term at the moment, and wondered if anyone could provide me with some information on how to specify a model with 2 (nested or cross-classified) random terms? Thanks, Jarrod.

Hi again. Perhaps a simple question this time.... I am analysing data with a dependent variable of insect counts, a fixed effect of site and two random effects, day, which is the same set of 10 days for each site, and then transect, which is nested within site (5 each). I am trying to fit the cross classified model using GLMM in lme4. I have, for potential use, created a second coding

Hi all, I have experimented a segmentation fault error using RMySQL_0.4-6 library in R 1.5.1. Look the sequence: ------------------------------------------- > library(RMySQL) > m <- dbManager("MySQL") > m <MySQLManager:(1137)> > describe(m) <MySQLManager:(1137)> Driver name: MySQL Max connections: 10 Conn. processed: 0 Default records per

Hello, I have a couple questions regarding generalized linear mixed models specifically around fitting the random effects terms correctly to account for any pseudo-replication. I am reading through and trying to follow examples from Zuur et al. Mixed Effects Models and Extensions in Ecology with R, but am still at bit unsure if I am specifying the models correctly. Background information: Our

Dear friends, I have asked last few days about cross-random effects using PQL, but I have not receive any answer because might my question was not clear. My question was about analysing the salamander mating data using PQL. This data contain cross-random effects for (male) and for (female). By opining MASS and lme library. I wrote this code sala.glmm <- glmmPQL(fixed=y~WSf*WSM,

Hi all! I am working with GLMM using the binomial family I use the following codes I dropped no significant terms, refitting the model and comparing the changes with likelihood: G.1<-lmer(data$Ymat~stu+spi+stu*sp1+(1|ber),data=data,family="binomial") G.1b<-lmer(data$Ymat~stu+spi+(1|ber),data=data,family="binomial") anova (G.1,G.2) But, when I want to evaluate the

Hello all, I would like to fit a mixed effects model, but my response is of the negative binomial (or overdispersed poisson) family. The only (?) package that looks like it can do this is glmm.ADMB (but it cannot run on Mac OS X - please correct me if I am wrong!) [1] I think that glmmML {glmmML}, lmer {Matrix}, and glmmPQL {MASS} do not provide this "family" (i.e. nbinom, or

I want to analyze binary, multinomial, and count outcomes (as well as the occasional continuous one) for clustered data. The more I search the less I know, and so I'm hoping the list can provide me some guidance about which of the many alternatives to choose. The nlme package seemed the obvious place to start. However, it seems to be using specifications from nls, which does non-linear

Dear R users, Does anyone knows how to run a glmm with one fixed factor and 2 random numeric variables (indices)? Is there any way to force in the model a separate interaction of those random variables with the fixed one? I hope you can help me. #eg. Reserve <- rep(c("In","Out"), 100) fReserve <- factor(Reserve) DivBoulders <- rep

Dear R gurus I am running a GLMM that looks at whether chimpanzees spend time in shade more than sun (response variable 'y': used cbind() on counts in the sun and shade) based on the time of day (Time) and the availability of shade (Tertile). I've included some random factors too which are the chimpanzee in question (Individual) and where they are in a given area (Zone). There are

Hello, I have a problem concerning estimation of GLMM. I used methods from 3 different packages (see program). I would expect similar results for glmm and glmmML. The result differ in the estimated standard errors, however. I compared the results to MASS, 4th ed., p. 297. The results from glmmML resemble the given result for 'Numerical integration', but glmm output differs. For the

I'm having trouble using binomial(link="cloglog") with GLMM in lme4, Version: 0.5-2, Date: 2004/03/11. The example in the Help file works fine, even simplified as follows: fm0 <- GLMM(immun~1, data=guImmun, family=binomial, random=~1|comm) However, for another application, I need binomial(link="cloglog"), and this generates an error for me: >

I'm trying to use GLMM in library(lme4), R 1.9.0pat, updated just now. I get an error message I can't decipher: library(lme4) set.seed(1) n <- 10 N <- 1000 DF <- data.frame(yield=rbinom(n, N, .99)/N, nest=1:n) fit <- GLMM(yield~1, random=~1|nest, family=binomial, data=DF, weights=rep(N, n)) Error in eval(expr, envir, enclos) : ..1 used in an incorrect

Hi R-Helpers: I?m trying to use the function GLMM from lme4 package, (R-1.8.1, Windows 98),and I get the following error: > pd5 = GLMM(nplant~sitio+ + fert+ + remo+ + sitio:fert+ + remo:sitio+ + remo:fert+ + remo:fert:sitio + data=datos, + family=binomial, +

I am trying to use the LME package to run a multilevel logistic model using the following code: ------------------------------------------------------------------------ ------------------------------------------- Model1 = GLMM(WEAP ~ TSRAT2 , random = ~1 | GROUP , family = binomial, na.action = na.omit ) ------------------------------------------------------------------------

Dear all, I want to evaluate several generalized linear mixed models, including the null model, and select the best approximating one. I have tried glmmPQL (MASS library) and GLMM (lme4) to fit the models. Both result in similar parameter estimates but fairly different likelihood estimates. My questions: 1- Is it correct to calculate AIC for comparing my models, given that they use

Greetings, May you please suggest a package or function to use for fitting a GLMM (generalized linear mixed model) with spatially correlated random effects? Thank you, Elijah DePalma [[alternative HTML version deleted]]