similar to: Linear Mixed Effects

PROC MIXED is used to fit mixed effects model for correlated data. Usually we can use either a REPEATED statment or a RANDOM statement. The random statement is corresponding to lme function in R -- specifying a random effect term. The repeated statement actually directly specifies the covariance structure -- is there a similar function in R to do this? I currently want to specify a unstructured

Thanks for the help Dimitris, However I still have a question, this time I'll be more specific, the following is my SAS code proc mixed data=Reg; class ID; model y=Time Time*x1 Time*x2 Time*x3 /S; random intercept Time /S type=UN subject=ID G GCORR V; repeated /subject = ID R RCORR; run; ** (Type =UN for random effects) The eqivalent lme statement I

I am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this model in R or should I bite the bullet and

Dear all, I am trying to simulate the null distribution for the likelihood ratio test statistic for testing 1 random effect versus no random effect. The asymptotic null distribution should be a mixture of a chi-squared distribution with 0 degrees of freedom and a chi-squared distribution with 1 degree of freedom. This means that I expect a point mass of 50% on 0 for the likelihood ratio

Dear R experts, I had a question which may not be directly relevant to R but I will be grateful if you can give me some advices. I ran a two-level multilevel model for data with repeated measurements over time, i.e. level-1 the repeated measures and level-2 subjects. I could not get convergence using lme(), so I tried MLwiN, which eventually showed the level-2 variances (random effects for

Hello. Slope estimates in lme are shrinkage estimates which pull the OLS slope estimates towards the population estimates, the degree of which depends on the group sample size and the distance between the group-based estimate and the overall population estimate. Although these shrinkage estimates as said to be more precise with respect to the true values, they are also biased. So there is a

As I understand it lme (in R v1.9.x) estimates random effect variances on a log scale, constraining them to be positive. Whilst this seems sensible, it does lead to apparently biased estimates if the variance is actually zero - which makes our simulation results look strange. Whilst we need to think a bit deeper about it - I still haven't got my head around what a negative variance could

Dear all, Suppose I have a linear mixed-effects model (from the package nlme) with nested random effects (see below); how would I present the results from the random effects part in a publication? Specifically, I?d like to know: (1) What is the total variance of the random effects at each level? (2) How can I test the significance of the variance components? (3) Is there something like an

I am trying to do a repeated measurement anova using an lme. I have the following variables: -ID, the identification of the individual -trail, with 2 levels -treatment, with 3 -time, measure 5 times the same individual -VCL, the response variable I tried the following in R, within.gr<-groupedData(VCL~time|ID/treatment/time,data=within)

I have an ANOVA model with 2 factors "Environment" and "Site", "Diameter" is the response variable. Site should be nested within Environment. Site is also a random factor while Environment is fixed. I can do this analysis using the "aov" function by using these commands: >model<-aov(Diam~Env+Error(Env%in%Site),data=environ) >summary(model)

Hi again, thank you for your previous answers. Just another question, though ... I get the following variance components after fitting a mixed model. Groups Name Variance Std.Dev. Corr PlantID TreatmCtrl 0.51784 0.71961 TreatmNoAccess 4.77469 2.18511 -0.063 TreatmNoKeel 4.22726 2.05603 0.513 0.751 TreatmNoSpur 0.45918

Hi folks, I was wondering how to run a mixed models approach to analyze a linear regression with a user-defined covariance structure. I have my model y = xa +zb +e and b ~ N (0, C*sigma_square). (and a is a fixed effects) I would like to provide R the C (variance-covariance) matrix I can easily provide an example, but at this point I am first trying to know what is the best package the

Hello, I am interested in using nlme to model repeated measurements, but I don't seem to get good CIs. With the code below I tried to generate data sets according to the model given by equations (1.4) and (1.5) on pages 7 and 8 of Pinheiro and Bates 2000 (having chosen values for beta, sigma.b and sigma similar to those estimated in the text). For each data set I used lme() to fit a model,

Dear all, I have tried to consider a correlation structure in lme (package lme4), but without success. I have used something like: > risul<-lme(y~x+ z , data=mydata, random=~ x | g, correlation = corAR1()) but the result is the same as: > risul<-lme(y~x+ z , data=mydata, random=~ x | g). Can anybody help me? Antonella ************************************************** Prof.

I am currently trying to get a "lme" analyses running to correct for the non-independence of residuals (using e.g. corAR1, corARMA) for a larger data set (>10000 obs) for an independent (lgeodisE) and dependent variable (gendis). Previous attempts using SAS failed. In addition we were told by SAS that our data set was too large to be handled by this procedure anyway (!!). SAS script

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 ) ------------------------------------------------------------------------

Hello, I have an unbalanced mixed model design with two fixed effects "site" (2 levels) and "timeOfDay" (4 levels) and two random effects "day" (3 consecutive days) and "trap" (6 unique traps, 3 per site). The dependent variable is the body length ("BL") of insect larvae from 7 to 29 individuals per trap (104 individuals in total). To account

Dear R list: I modelled changes in a variable (mconc) over time (d) for individuals (replicate) given one of three treatments (treatment) using: mconc.lme <- lme(mconc~treatment*poly(d,2), random=~poly(d,2)|replicate, data=my.data) summary(mconc.lme) shows that the linear coefficient of one of the treatments is significantly different to zero, viz. Value Std.Error

Hi, I'm confused about how to specify random and fixed factors in an aov() term. I tried to reproduce a textbook example: One fixed factor (Game, 4 levels) and one random factor (Store, 12 levels), response is Points. The random factor Store is nested in Game. I tried > str(kh.df) `data.frame': 48 obs. of 4 variables: $ Subj : Factor w/ 48 levels

I am analyzing a dataset on the effects of six pesticides on population growth rate of a predatory mite. The response variable is the population growth rate of the mite (ranges from negative to positive) and the exploratory variable is a categorical variable (treatment). The experiment was blocked in time (3 blocks / replicates per block) and it is unbalanced - at least 1 replicate per block. I am