similar to: Random specification in LMER

Hello, This is a follow up question to my previous one http://tolstoy.newcastle.edu.au/R/e4/help/08/02/3600.html I am attempting to model relationship satisfaction (MAT) scores (measurements at 5 time points), using participant (spouseID) and couple id (ID) as grouping variables, and time (years) and conflict (MCI.c) as predictors. I have been instructed to include random effects for the

Hi, I would like to know whether it is possible to obtain a value of significance for random effects when aplying the lme or related functions. The default output in R is just a variance and standard deviation measurement. I feel it would be possible to obtain the significance of these random effects by comparing models with and without these effects. However, I'm not used to perform

Hello, I have two very rudimentary questions regarding the random effects terms in the lme and lmer functions. I apologize if this also partly strays into a general statistics question, but I'm a bit new to this all. So hopefully it'll be a quick problem to sort out... Here is my experimental setup: I raised butterflies in 5 different testing chambers all set to different

I am having an issue with lmer that I wonder if someone could explain. I am trying to fit a mixed effects model to a set of longitudinal data over a set of individual subjects: (fm1 <- lmer(x ~ time + (time|ID),aa)) I quite often find that the correlation between the random effects is 1.0: Linear mixed model fit by REML Formula: x ~ time + (time | ID) Data: aa AIC BIC logLik deviance

Hi I want to extract the random slopes from a lmer (I am doing a random regression), but are the answers obtained from ranef or coef? My model is: mod1<-lmer(B~ A +(A|bird), family=quasibinomial) And I want to obtain a slope for each individual bird but am not sure which output I need and can't find the answer anywhere. Thanks Sam Dr Samantha Patrick EU INTERREG Post Doc Davy 618

Hi all, I am not a very proficient R-user yet, so I hope I am not wasting people?s time. I want to run a linear mixed model with 3 random factors (A, B, C) where A and B are partially crossed and C is nested within B. I understand that this is not easily possible using lme but it might be using lmer. I encountered two problems when trying: Firstly, I can enter two random factors in lmer but

Hi, all, I have a question about random effects model. I am dealing with a three-factor experiment dataset. The response variable y is modeled against three factors: Samples, Operators, and Runs. The experimental design is as follow: 4 samples were randomly chosen from a large pool of test samples. Each of the 4 samples was analyzed by 4 operators, randomly selected from a group of

Dear expeRts, there is obviously a general trend to use model comparisons, LRT and AIC instead of Wald-test-based significance, at least in the R community. I personally like this approach. And, when using LME's, it seems to be the preferred way (concluded from postings of Brian Ripley and Douglas Bates' article in R-News 5(2005)1), esp. because of problems with the d.f. approximation.

I have data from a design in which items are completely nested within subjects. Subject is the only second-level factor, but I have multiple first-level factors (IVs). Say there are 2 such independent variables that I am interested in. What is the proper syntax to fit a mixed-effects model with a by-subject random intercept, and by-subject random slopes for both the 2 IVs? I can

Hi All, I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA

Paul: I may have found the issue (which is similar to your conclusion). I checked using egsingle in the mlmRev package as these individuals are strictly nested in this case: library(mlmRev) library(nlme) fm1 <- lme(math ~ year, random=~1|schoolid/childid, egsingle) fm2 <- lmer(math ~ year +(1|schoolid:childid) + (1|schoolid), egsingle) Checking the summary of both models, the output is

Using ranef() (package nlme, version 3.1-75) with an 'lme' object I can obtain random effects for intercept and slope of a certain level (say: 1) - this corresponds to (say level 1) "residuals" in MLWin. Maybe I'm mistaken here, but the results are identical. However, if I try to get the standardized random effects adding the paramter "standard=T" to the

Hi, I am an R beginner and having problems structuring my REML models. I have a model with y=weight x1=time x2=timesquared id=individual identity I need to structure the model such that in the random effects there is a constant intercept for all individuals but a separate individual slope for both x1 and x2 (a coefficient score for every individual).

Dear all, I have the following output generated by linear regression. Since there is only one regression intercept and one slope for one set of data, what is the meaning of std. error for intercept and that of slope? Thanks in advance. Sincerely, Minghua > data(thuesen) > attach(thuesen) > lm(short.velocity~blood.glucose) Call: lm(formula = short.velocity ~ blood.glucose)

Dear all, I'm attempting to use a piecewise regression to model the trajectory of reproductive traits with age in a longitudinal data set using a mixed model framework. The aim is to find three slopes and two points- the slope from low performance in early age to a point of high performance in middle age, the slope (may be 0) of the plateau from the start of high performance to the

Greetings. Here is sample code, with some comments. It shows how I can simulate data and estimate glm with binomial family when there is no individual level random error, but when I add random error into the linear predictor, I have a difficult time getting reasonable estimates of the model parameters or the variance component. There are no clusters here, just individual level responses, so

Dear Thierry, thank you for the quick reply. I have only one question about the approach you proposed. As you suggested, imagine that the model we end up after the model selection procedure is: mod2.1 <- lmer(dT_purs ~ T + Z + (1 +T+Z| subject), data =x, REML= FALSE) According to the common procedures specified in many manuals and recent papers, if I want to compute the p_values relative to

Hello, I am looking for a way to obtain standard errors for emprirical Bayes estimates of a model fitted with lmer (like the ones plotted on page 14 of the document available at http://www.eric.ed.gov/ERICDocs/data/ericdocs2/content_storage_01/0000000b/80/2b/b3/94.pdf). Harold Doran mentioned (http://tolstoy.newcastle.edu.au/~rking/R/help/05/08/10638.html) that the posterior modes' variances

Dear all, I want to estimate a crossed-random-effects model (i.e., measurements, students, schools) where students migrate between schools over time. I'm interested in the fixed effects of "SES", "age" and their interaction on "read" (reading achievement) while accounting for the sample design. Based on a previous post, I'm specifying my model as: fm1 <-

This question comes up periodically, probably enough to give it a proper thread and maybe point to this thread for reference (similar to the 'conservative anova' thread not too long ago). Moving from lme syntax, which is the function found in the nlme package, to lmer syntax (found in lme4) is not too difficult. It is probably useful to first explain what the differences are between the