search for: raudenbush

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

...ividuals ( in this instance the impact of the GDP per capita on the attitudes towards the EU enlargement) by allowing national differences in both slopes (GDP per capita) and interceps. In R programm for the fitting the hierarchical models i can use the nlme package. I found a literature (Bryk and Raudenbush) for the hierarchical models and understood how to build this models by using the survey data. The question arise if I?m thinking about the combination of the datasets: the GDP per capita that will be hold as a constant and the survey data for each respondent. My question is how I could solve this...

...unction 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 and Raudenbush's Hierarchical Linear models: From R: > attach(Bryk) > cses <- meanses <- numeric(length(ses)) # initialize > for (sc in unique(school)) { + meanses[school==sc] <- mean(ses[school==sc]) + cses[school==sc]<-ses[school==sc]- meanses[school...

Hi, all. I'm trying to use some linear regression models in which both the dependent and independent variables are measured with some error. To make things worse, while the errors in the dependent variable are uniform, the errors in the independent (or explanatory, or "x") variables can be heteroskedastic. I've been looking at the book _Measurement Error Models_ by Fuller

...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 conservative ANOVA test in their book. I have also consultet Raudenbush Bryk on the subject and they suggest using a Chi sqare statistics. It is defined as follows: SUM by j( (beta_hat_qj - y_hat_q0 - sum(y_hat_qs*w_sj))^2/V_hat_qqj) beta are the within 2-level coffecients - got them with the coef() y is a fixed effect or grand mean the sum is for accounting of the l...

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

...zed Within-Group Residuals: Min Q1 Med Q3 Max -1.2951648 -0.8865510 -0.7183326 1.0428044 1.6135857 Number of Observations: 1042 Number of Groups: groupid participantid %in% groupid 20 137 Raudenbush & Bryk (1992; 2002) suggest that the Intraclass Correlation is a useful statistic for a hierarchical linear model. My understanding is that this statistic is the proportion of the model's total variance that is "explained" by each level of the model. I have calculated this for lev...

...r(PID)), data = PCP, random =~ (1|fPID), family = poisson (link="log")) summary(M2$gam) summary(M2$mer) It is not clear to me whether either of these gives me what I want. In generalized linear mixed models, I am accustomed to the HLM approach (e.g., Raudenbush & Bryk) where each case would have a trend coefficient, and the random effect would tell me if those four coefficients varied significantly. So that is what I am looking for, but adding the nonlinearity modeling of GAM. Is either of these formulations giving me what I want--a test of whethe...

Un texte encapsul? et encod? dans un jeu de caract?res inconnu a ?t? nettoy?... Nom : non disponible Url : https://stat.ethz.ch/pipermail/r-help/attachments/20070404/0f61f54a/attachment.pl

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) +

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