Practice session5HLM多层线性模型讲义.docVIP

R-practice session 5 CSSS 560 Marijtje van Duijn Winter 2006 The commands used in this session are available as R syntax file (Session5.R) at the website. Data input and preparation We use the data as provided in example 12.1 of Snijders Bosker. The dependent variable is student evaluations of 51 teachers over 4 years. The data are very unbalanced. Explanatory variables are teacher experience (coincides with ‘time’) own evaluation and gender. After getting the necessary libraries, download the data file SBbook12.csv from the class website. Also get the file session5.r and execute the commands under data preparation. Note that a groupedData object ‘longdata’ is created. Some more commands for investigating grouped data We can extract some useful information about the grouping with the following commands: table(getGroups(longdata)) getGroups(longdata) unique(getGroups(longdata)) Another useful trick in this case may be to split the data in separate objects for men and women. This is especially attractive for making pictures. (The same can be done for the men; see the file session5.R) longdataf-groupedData(prox_stu~occ|teacher,data=subset(data12,gender0), +labels = list(x=time, y=proximity score)) Estimating multilevel models for longitudinal data One of the important questions in modeling multilevel data is how to treat ‘time’. In this case with fixed occasions we have to choose what kind of ‘contrasts’ we want to have. In the example in the book, a ‘dummy’ coding is used, giving separate estimates for the means at each time point. Here we will also explore other contrasts that are statistically equivalent, but lead to different parameter estimates. We will start with the ‘empty’ compound symmetry model (or random intercept model) with equal means for all time points (model 1 in table 12.1). model1.1-lme(prox_stu~1,data=longdata,random=~1|teacher) summary(model1.1) We then estimate three different specifications for model 2: P for polynomial, F for f