similar to: how to specify uncorrelated random effects in nlme::lme()

Hi, all! How to setup own i686 mock for CentOS7? Or, is there any public i686 repo for CentOS7? I found i686 repo available in internal CentOS building environment, from a root.log from a mock build result[1]. [1] http://buildlogs.centos.org/c7-updates/glibc/20141218212615/2.17-55.el7_0.3.i386/root.log Cheers, -robin

I'm analizing the Argentina stock market (merv) I download the data from yahoo library(tseries) Argentina <- get.hist.quote(instrument="^MERV","1996-10-08","2003-11-03", quote="Close") merv <- na.remove(log(Argentina)) I made the Augmented Dickey-Fuller test to analyse if merv have unit root: adf.test(merv,k=13) Dickey-Fuller = -1.4645,

I'm analizing the Argentina stock market (merv) I download the data from yahoo library(tseries) Argentina <- get.hist.quote(instrument="^MERV","1996-10-08","2003-11-03", quote="Close") merv <- na.remove(log(Argentina)) I made the Augmented Dickey-Fuller test to analyse if merv have unit root: adf.test(merv,k=13) Dickey-Fuller = -1.4645,

Ok I made Jarque-Bera test to the residuals (merv.reg$residual) library(tseries) jarque.bera.test(merv.reg$residual) X-squared = 1772.369, df = 2, p-value = < 2.2e-16 And I reject the null hypotesis (H0: merv.reg$residual are normally distributed) So I know that: 1 - merv.reg$residual aren't independently distributed (Box-Ljung test) 2 - merv.reg$residual aren't indentically

Hello, is it possible to create two uncorrelated random vectors for a given distribution. In fact, I would like to have something like the function "rnorm" or "rlogis" with the extra property that they are uncorrelated. Thanks for your help, Luba [[alternative HTML version deleted]]

Hi List, I have to compare a relationship between y and x for two locations. I found logistic regression fits both datasets well, but I am not sure how to test if relationships for both sites are significantly different. I searched the r site, however no answers exactly match the question. I used Tukey's HSD to compare two means, but the relationship in my study was not simply linear. So I

Hello. I want to specify a diagonal structure for the covariance matrix of random effects in the lme() function. Here is the call before I specify a diagonal structure: > fit2<-lme(Ln.rgr~I(Ln.nar-log(0.0011)),data=meta.analysis, + random=~1+I(Ln.nar-log(0.0011)|STUDY.CODE,na.action=na.omit) and this works fine. Now, I want to fix the covariance between the between-groups slopes

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error message : Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1

Among others, datam contains the columns: logconc, tm, dose, subj, bilirubin. None of these are factor variables. The following compartment models work (the first still has not converged after 100 interations): res1 <- nlme(logconc~p2+p3+log(dose/(exp(p1)-exp(p2))* (exp(-exp(p2)*tm)-exp(-exp(p1)*tm))),start=list(fixed=c(5,-2,-0.1)), fixed=list(p1+p2+p3~1),control=list(maxIter=100),

Hi, everyone, I wrote a function, which includes an nlme estimation. The problem is sometimes nlme may not converge due to too many random effects. Say a, b are two parameters. if I specify random effects by: random = a+b~1, nlme fails to converge. Then I have to constrain the random effects in a positive definite diagonal matrix by: random = list(pdDiag(a+b~1)) My question is how I can

Alguien tiene experiencia en escribir una pdMat. Para aquellos que no lo recuerden son las matrices de covarianzas de los efectos aleatorios que ajusta la función lme de la librería nlme Estas matrices tiene especial importancia en aplicaciones de genética de poblaciones y en particular en mapeo de asociación. Pinheiro y Bates dicen que el usuario puede crear sus propias pdMat y sugiere como

Dear R users, I'm trying to specify a generalized linear mixed model in R, basically a Poisson model to describe monthly series of counts in different regions. My aim is to fit subject-specific curves, modelling a non-linear trend for each region through random effects for linear splines components (see Durban et al, Stat Med 2005, or " Semiparametric regression" by Ruppert et al,

Dear list, I am trying to use lme to build the analogue of the following SAS MIXED random specification: random int+Variable1+Variable2 /subject = Subject group=Condition type=vc; which gives a Condition-blocked heterogeneity in the random effects variance-covariance matrix. Needless to say, I have a hard time in specifying Condition-specific heterogeneities in the variance-covariance

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

Hi, Here's how my epel-7-i386.cfg mock file looks like: http://fpaste.org/164110/19877702/raw/ Do note the 32bit packages are unofficial and unsupported. RedHat does not support 32bit in EL7. HTH Lucian -- Sent from the Delta quadrant using Borg technology! Nux! www.nux.ro ----- Original Message ----- > From: "Robin Lee" <robinlee.sysu at gmail.com> > To: centos at

library(nlme) data(Phenobarb) na.include <- function(x)x phe1 <- nlme(conc~phenoModel(Subject, time, dose, lCl, lV), data = Phenobarb, fixed = lCl+lV~1, random= pdDiag(lCl+lV~1), start = c(-5,0), na.action = na.include, naPattern = ~!is.na(conc)) phe.ranef <- ranef(phe1,augFrame=TRUE) plot(phe.ranef, form=lCl~Wt+ApgarInd) [Error in max(length(x0),

Dear R users, I am using nlme to fit a pharmacokinetic model. The base model is parameterized in terms of CL, V1, V2 and Q. basemodel<-nlme(Conc ~TwoCompModel(CL,Q,V1,V2,Time,ID), data = data2, fixed=list(CL+Q+V1+V2~1), random = pdDiag(CL+V1+V2~1), start=c(CL=log(20),Q=log(252),V1=log(24.9),V2=log(120)), control=list(returnObject=TRUE,msVerbose=TRUE, msMaxIter=20,pnlsMaxIter=20,pnlsTol=1),

I have a problem moving from multistratum aov analysis to lmer. My dataset has observations of ampl at 4 levels of gapf and 2 levels of bl on 6 subjects levels VP, with 2 replicates wg each, and is balanced. Here is the summary of this set with aov: >> summary(aov(ampl~gapf*bl+Error(VP/(bl*gapf)),hframe2)) > >Error: VP > Df Sum Sq Mean Sq F value Pr(>F) >Residuals

Dear all, I am studying the possibility of using the nlme package in R to analyse field trials of agricultural crops. I have a problem with the syntax for the modelling of variance covariance structures. I can model the within-group covariance structure using the correlation argument and the covariance structure between different random effects of the same grouping level using

Hi, Nux! Can you re-paste this cfg? The url is outdated. - robin On Tue, Dec 30, 2014 at 2:30 AM, Nux! <nux at li.nux.ro> wrote: > Hi, > > Here's how my epel-7-i386.cfg mock file looks like: > http://fpaste.org/164110/19877702/raw/ > > Do note the 32bit packages are unofficial and unsupported. RedHat does not > support 32bit in EL7. > > HTH > Lucian >