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Conducting Bayesian Analysis Online Using BUGS
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Built-in models:Conditional linear growth curve models
!Model A growth curve model is a two-level model. For the first level, we have {{%% \[ y_{it}=b_{i0}+b_{i1}t+e_{it} \] %%}} where, \(y_{it}\) is the observed data for subject \(i\) at time \(t\), \(b_{i0}\) and \(b_{i1}\) are intercept and slope, respectively, for subject \(i\). Suppose there are two subject level predictors \(x_{1i}\) an \(x_{2i}\). At the second level, we have {{%% \[ b_{i0} = \beta_{1}+\beta_{2}x_{1i}+\beta_{3}x_{2i}+v_{i0} \] \[ b_{i1} = \beta_{4}+\beta_{5}x_{1i}+\beta_{6}x_{2i}+v_{i1} \] %%}} We further assume that \(Var(e_{it})=\sigma^2\) and \(\mathbf{b}_i=(b_{i0}, b_{i1})'\) follows a bivariate normal distribution with mean '''0''' and covariance matrix '''D'''.
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. Last changed: 2014/11/12 02:47
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WebBUGS