similar to: HLM in R (fwd)

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

An executable version 0.6 of my libraries is now available at www.luc.ac.be/~jlindsey/rcode.html This works with MS R0.64.2 and appears possibly to work with R65.0. There is a serious problem with the Fortran compiler as some of the examples for elliptic and carma crash it. These same examples do not crash R63.0 with the library executables of Jan 99. I am releasing this anyway because of the

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

I have placed a corrected executable of my growth library for MS-Windows at www.luc.ac.be/~jlindsey/rcode.html This now runs Peter Mandeville's example for carma and the example for elliptic from my docs. This Fortran compiler is clearly having serious problems with small double precision numbers that it sets exactly to zero. I have had to put in checks for this. I have not had any similar

R Users, Jim Lindsey's R packages: * event: Event History Procedures and Models * gnlm: Generalized Nonlinear Regression Models * growth: Multivariate Normal and Elliptically-Contoured Repeated Measurements Models * repeated: Non-Normal Repeated Measurements Models * rmutil: Utilities for Nonlinear Regression and Repeated Measurements Models * stable: Probability Functions and

R Users, Jim Lindsey's R packages: * event: Event History Procedures and Models * gnlm: Generalized Nonlinear Regression Models * growth: Multivariate Normal and Elliptically-Contoured Repeated Measurements Models * repeated: Non-Normal Repeated Measurements Models * rmutil: Utilities for Nonlinear Regression and Repeated Measurements Models * stable: Probability Functions and

> To sum up, I can't figure out how MLWin calculates the > standardized residuals. But I understand that this is not a > question for the R list. > Nevertheless, it would help if someone could point me to some > arguments why not to use them and stick to the results > obtainable by ranef(). Hi Dirk: Well, it is interesting that mlWin and lmer generate the same exact

When playing around fitting unconditional growth models using R and MlwiN today, I produced two different sets of estimates that I can't reconcile and wondered if anyone here has an idea: The data is two-level repeated measures data with measures nested within child. There are two measures per child. I've fit an unconditional growth model as in Singer and Willet (2003) that allows for

Doug, I thought perhaps that you might be interested in the comparison of lme to the results for the same models fitted by Richard Jones' carma (I just wrote the R interface to his Fortran code). The code to run the example from the lme help and for the equivalent with carma is in the file below. The two main differences in results are 1. the random coefficients covariance matrix is quite

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

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

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

IIRC, the license stated in the source code of dataplot is rather restrictive. I don't think one is allowed to use the source as can be done with GPL code. Cheers, Andy > -----Original Message----- > From: Brett A Magill [mailto:Brett.Magill at slps.org] > Sent: Thursday, June 27, 2002 1:14 PM > To: r-help at stat.math.ethz.ch > Subject: [R] FW: re: GUI's for teaching

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

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

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

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

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).