search for: bryk

...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, mean) > detach(MathAchieve) > Bryk <- as.data.frame(MathAchieve[, c("School", "SES", "MathAch")]) > names(Bryk) <- c("school", "ses", "mathach") > sample20 <- sort(sample(7185, 20)) > Bryk$meanses <- mses[as.character(Bryk$school)] > Bryk[sample2...

...he 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 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]-...

...is originalmodel <- lm(log(v.dep) ~ log(v2) +log(v4) + v3 + v5+ v6, data=data) (The thing is that I don't know how to specify that groups are in v5, where to put in the lmList statement |v5) By the way, following Fox pdf I was trying as well to group my data, as this example: /attach(Bryk) cat <- sample(unique(school[sector==?Catholic?]), 20) Cat.20 <- groupedData(mathach ~ ses | school, data=Bryk[is.element(school, cat),]) pub <- sample(unique(school[sector==?Public?]), 20) Pub.20 <- groupedData(mathach ~ ses | school, data=Bryk[is.element(school, pub),])/ what am I d...

...ts on individuals ( 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 c...

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

...riability 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 level...

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

...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 level 2 as 0.3...

...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 whether trend dif...

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