similar to: very basic HLM question

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

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

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

Hi, I'm trying to analyse some data I have imported into R from a .csv file but when I carry out the aov command the results show only one degree of freedom when there should be 14. Does anyone know why? I'd really appreciate some help, the data is pasted below. /The imported table looks ike this this:/ Order Transect Sample Abundance 1 Coleoptera 1 1 13 2

Dear list In HLM, one can specify a covariate at one of the "levels". For example, if the data structure are repeated observations nested within students nested within schools, school size might be a covariate that is used at level 3, but not at the other levels. In HLM this is rather easy to do. However, how can one specify a covariate in R for only one of the levels? I have a

Dear List: I was able to run the following in nlme successfully, but the same model and code (same dataset) failed to run in lme4 and gave me the error message below. Any thoughts? lme(math~year, data=egsingle, random=~year|schoolid/childid) Error in lme(formula = math ~ year, data = egsingle, random = structure(list( : Unable to invert singular factor of downdated X'X

Hi All, The data set that I have is a cluster data, and I want to run a HLM mixed model with multi-level response. Here is my data set: response: - Level (num: 1, 2, 3, 4, 5 - 5 levels) Covariates: - Type (Factor: A, B, C - 3 levels) - yr (num: 2006, 2007, ...) - Male (num: 0=not Male, 1=Male - 2 levels) - Ethnicity (Factor: A, B, H, ..., - 7 levels) - ELL (num: 0, 1, - 2

Hi, I am trying to find the min of day for each student in each year. Here is the dataset: date studentid year 1/1/05 6:07 AM 236 20082009 3/27/09 9:45 AM 236 20082009 4/29/09 8:44 AM 236 20082009 3/27/09 11:36 AM 310 20082009 4/1/09 10:43 AM 310 20082009 10/15/09 8:54 AM 310

Dear List: I am having difficulties with the fitted values at different levels of a multilevel model. My data set is a series of student test scores over time with a total of 7,280 observations, 1,720 students nested witin 60 schools. The data set is not balanced. The model was fit using eg.model.1<-lme(math~year, random=~year|schoolid/childid, data=single). When I call the random

Hello R-Helpers, I was trying to use the lmList function to get the lmList graphic similar to Pinheiro and Bates (pg 33). I did not have a problem creating the graphic when I used the Orthodont data frame or 2 other data sets when there are no missing values. My data has missing values. Do I need to remove the missing values before the lmList function will work? for a small example: > a

Hello, I have problems interpreting the results of a Friedman test. It seems to me that the p-value resulting from a Friedman test and with it the "significance" has to be interpreted in another way than the p-value resulting from e.g. ANOVA? Let me describe the problem with some detail: I'm testing a lot of different hypotheses in my observer study and only for some the premises

Dear list: I am trying to merge two files together from output I get based on the coef() command. Here is what I am running into. I have two simple linear mixed models > mod1.lme<-lme(math~year, data=sample, random=~year|childid/schoolid) > mod2.lme<-lme(math~year, data=sample, random=~year|childid) I then call the coefficients and store them in the following objects using >

Dear R community, I'm currently using R to analyze functional Magnetic Resonance Imaging data. Each analysis involves running ~120,000 repeated-measures ANOVAs. I would like to know if there is any automatic way to access the F- and P-value data that are associated with each of these 120,000 ANOVAs. For example, if the summary output (for the 1st ANOVA of 120,000) shows the following value

Greetings, I am interested in creating a stepwise fixed order regression function. There's a function for this already called add1( ). The F statistics are calculated using type 2 anova (the SS and the F changes don't match SPSS's). You can see my use of this at the very end of the email. What I want: a function to make an anova table with f changes and delt R^2. I ran into

I have Data like this: Tone OQtil4 OQ0 GOtil4 GO0 SKrhsJ SK0 RCrhsJ RC0 1 HsLG -9.1347877 -2.97 -7.409590 -6.40 9.389357 20.60 10.688854 24.40 2 HsLG -7.9270569 -2.03 -7.861541 -6.90 10.165324 26.40 10.640183 23.10 3 HsLG -7.0394106 -1.26 -7.509566 -6.53 12.033194 30.87 9.401959 20.37 4 HsLG -6.8625610 -1.03 -7.645118 -6.68 10.372605 24.22 9.378803 17.82 5

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

Win7, R2.11.0 I am working on a report together with several co-authors. The data concern several performance measures on a set of groups. These measures are scored by external judges. We report findings on several datasets, including several of the 6 ICC's discussed by Shrout & Fleiss (1979). We determine these using the icc function from the irr package. We have also used the ICC

I've rolled up R-0.99.0.tgz a moment ago. You can get it from ftp://cvs.r-project.org/pub/CRAN/src/base/R-0.99.0.tgz or http://cvs.r-project.org/pub/CRAN/src/base/R-0.99.0.tgz or wait for it to be mirrored at a CRAN site near you within a day or two. There's also a version split in three (!) for floppies if you prefer that. The next release is to be 1.0.0 at the end of this month,

I've rolled up R-0.99.0.tgz a moment ago. You can get it from ftp://cvs.r-project.org/pub/CRAN/src/base/R-0.99.0.tgz or http://cvs.r-project.org/pub/CRAN/src/base/R-0.99.0.tgz or wait for it to be mirrored at a CRAN site near you within a day or two. There's also a version split in three (!) for floppies if you prefer that. The next release is to be 1.0.0 at the end of this month,

Does anyone know of code to conduct hierarchical (that is, multi-level) models using R. Beyond simply requiring a nested design, I want to model explicitly the covariance between levels as is done in such multi-level modeling software as HLM or MLwin and discussed in Goldestein (1999) available online at http://www.arnoldpublishers.com/support/goldstein.htm (a nice and free resource for anyone