similar to: regression with uncertainty in both variables

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

Dear all, I have the following question. I have to fit the hierarchical model for the hypothesis concern the individual-level effects by controlling for the individual -level attributes and national-level contextual effects on individuals by using R. O have to obtain the estimates of the impact of the second-level (national: GDP per capita) effects on individuals ( in this instance the impact

Hi I was asked if lme can use FIML (Full Information Maximum Likelihood) instead of REML or ML but I don't know the answer. Does anybody know if this is implemented in R? Thanks Francisco

Dear R list members, I've turned up a strange discrepancy between results obtained from the lme function in the nlme package in R and results obtained with lme in S-PLUS. I'm using version 3.1-24 of nlme in R 1.4.1 under Windows 2000, and both S-PLUS 2000 and 6.0, again under Windows 2000. I've noticed discrepancies in a couple of instances. Here's one, using data from Bryk

Hi, does anyone out there have a recommendation for multilevel / random effects and longitudinal analysis? My dream book would be something that's both accessible to a non-statistician but rigorous (because I seem to be slowly turning into a statistician) and ideally would use R. Peter

Dear r-devel list members, Dirk Eddelbuettel brought the following problem to my attention. The code is abstracted from the appendix on mixed models from my R and S-PLUS Companion: > set.seed(12345) # for reproducibility > library(nlme) Loading required package: lattice > data(MathAchieve) > data(MathAchSchool) > attach(MathAchieve) > mses <- tapply(SES, School,

data from multilevel units,first sample the class ,and then the student in calss.following is the 2-level model. and the level-1 model deals with the student,and the level-2 model deals with the class level the students belong to. Level-1 Model Y = B0 + B1*(ZLEAD) + B2*(ZBUL) + B3*(ZSHY) + R Level-2 Model B0 = G00 + U0 B1 = G10 + G11*(ZWARMT) + U1 B2 = G20 + G21*(ZWARMT) + G22*(ZABLET) +

In a classical meta analysis model y_i = X_i * beta_i + e_i, data {y_i} are assumed to be independent effect sizes. However, I'm encountering the following two scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels

Hi all, I've just started working my way through Mike West and Jeff Harrison's _Bayesian Forecasting and Dynamic Models_, and I was wondering if there were any publically-available packages to handle dynamic linear models, as they describe. I found the "dynlm" package, but either I don't yet understand what's going on or that package uses a different sense of the phrase

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 R-Helpers, I posted two days ago on testing significance of random effects in mgcv, but realize I did not make my overall purpose clear. I have a series of N short time series, where N might range from 3-10 and short means a median of 20 time points. The sample data below (PCP) has N = 4 cases with 9, 13, 16 and 16 observations over time respectively. The data set contains four

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:

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",

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 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

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

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

HLM question? Is there a minmum number of observations required for a category..I have individusals in work teams.I have incomplete data for all the teams ..sometimes I only have data for one person in a team.I assume that HLM can't work here! But what would be the mimimal.at the moment I have a sample of about 240 in about 100 teams with teamsizes form 2 to 5. Any advice? Thanks