similar to: NLME Covariates

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

Do you know if you can map in R? I have my minimum spanning tree, but as there are 1371 nodes (all over Australia) I'd like to be able to "graph" them as they actually would be on the map. Do you know if this is possible?

Hi R users! I am trying to learn some multilevel analysis, but unfortunately i am now very confused. The reason: http://www.ats.ucla.edu/stat/hlm/seminars/hlm_mlm/mlm_hlm_seminar.htm http://www.ats.ucla.edu/stat/sas/seminars/sas_mlm/mlm_sas_seminar.htm and MlmSoftRev. pdf from mlmRev package. >From what i see, the first two links seem to declare the level one variable as a random part (i

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

Dear R-users, I'm running a lmer model using the lme4 package. My dependent variable is dichotomous and I'm using the "binomial" family. The results are slightly different from the HLM results based on a Bernoulli distribution. I read that a Bernoulli distribution is an extension of a binomial distribution. Is that right? If so, how can I adapt my R model to a Bernoulli

I'm hoping that this is a relatively easy question for someone familiar with the lme4 package. I'm accustomed to using HLM software and writing a simple 2 level [null] equation like this: L1 - Yij = b0 + e L2 - b0 = B00 + u0 The following command in R provides results that are identical to the HLM program. results <- lmer( Y ~ 1 |id , PanelData4) I can't

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 am trying to convert SAS codes to R, but some of the result are quite different from SAS. When I ran proc mixed, I have an option ddfm=bw followed by the model. How can I show this method in R?(I am thinking that this maybe the reason that I can't get the similar results) below is my SAS codes: proc mixed data=test covtest empirical; class pair grade team school; model score = trt

Full_Name: Zivan Karaman Version: 2.7.2 OS: Windows XP Submission from: (NULL) (195.6.68.214) I'm puzzled by the readRegistry function. Shouldn't the "hive" argument be something like c("HLM", "HCR", "HCU", "HU", "HCC", "HPD") rather than c("HLM", "HCR", "HCU", "HU",

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

Hello dear uesRs, I am working on modeling both level one and level two heteroscedasticity in HLM. In my model, both error variance and variance of random intercept / random slope are affected by some level two variables. I found that nlme is able to model heteroscedasticity. I learned how to use it for level one heteroscedasticity but don't know how to use it to model the level

If you don't get a response it is because you did not read the Posting Guide which indicates that the R-sig-ME mailing list is where this question would have been on-topic. -- Sent from my phone. Please excuse my brevity. On August 16, 2017 6:17:03 AM PDT, b88207001 at ntu.edu.tw wrote: >Hello dear uesRs, > >I am working on modeling both level one and level two

This allows us to advertise more modes especially on HDR displays. Fixes using 4K at 60 modes on my TV and main display both using a HDMI to DP adapter. Also fixes similiar issues for users running into this. Cc: stable at vger.kernel.org # 5.10+ Signed-off-by: Karol Herbst <kherbst at redhat.com> --- drivers/gpu/drm/nouveau/dispnv50/disp.c | 32 +++++++++++++++++++++++++

A better place for this post would be on R's mixed models list: r-sig-mixed-models . Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Aug 16, 2017 at 6:17 AM, <b88207001 at ntu.edu.tw> wrote: > Hello dear

Dear R listers, I know I'm breaking the rules by asking a "homework" related question-- I hope you'll forgive me. I am a social psychology graduate student, and the only one in my department who uses R. I successfully completed my multiple regression and structural equation modeling courses using R (John Fox's car and sem packages were a big help, as was his book).

> On Tue, 12 Sep 2000, Magill, Brett wrote: > > > 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) > > The lme()

I have successfully installed R (version 2.8.1) and RMySQL (version 0.7-4) on my PC (Windows). Now I have a problem running RMySQL on R. I constantly receive the following error-message: Error in utils::readRegistry("SOFTWARE\\MySQL AB", hive = "HLM", maxdepth = 2) : Registry-Schl?ssel 'S' nicht gefunden Error : .onLoad in 'loadNamespace' f?r

Dear R-helpers, I'm analyzing a data with hierarchical linear model. I have one level 1 predictor and one level 2 predictor, which looks like below: fm1 <- lmer(y ~ 1 + x1 + x2 + x1:x2 + (1 + x1 | id.full)) where: y is the outcome variable. x1 is the level 1 predictor variable. x2 is the level 2 predictor variable. id.full is the conditioned variable. It runs beautifully when only x1

Hi everyone, I need to get a between-component variance (e.g. random effects Anova), but using lmer I don't get the same results (variance component) than using random effects Anova. I am using a database of students, clustered on schools (there is not the same number of students by school). According to the ICC1 command, the interclass correlation is .44 > ICC1(anova1) [1] 0.4414491

Dear listers, I can't find the variance or se of the coefficients in a multilevel model using lme. I want to calculate a Chi square test statistics for the variability of the coefficients across levels. I have a simple 2-level problem, where I want to check weather a certain covariate varies across level 2 units. Pinheiro Bates suggest just looking at the intervals or doing a rather