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// simulated data | |
// y = depression, t=timepoint (1 to 3), therapist=therapistcluster, id=patient | |
// 3 level model: observations nested in people in therapists | |
mixed y t || therapist: t, nocons || id:, stddev | |
// same model (???) using stata bayesian model fit | |
bayesmh y i.id i.therapist i.therapist#c.t, likelihood(normal({var_residual})) noconstant /// | |
prior({y:_cons}, normal(0,100)) /// | |
prior({var_therapist}, igamma(.001, .001)) /// | |
prior({var_u}, igamma(0.001,0.001)) /// | |
prior({var_residual}, igamma(.001, .001)) /// | |
prior({y:i.id}, normal({y:_cons}, {var_u})) /// | |
prior({y:i.therapist}, normal(0, 100)) /// | |
prior({y:t}, normal(0, 100)) /// | |
prior({y:i.therapist#c.t}, normal({y:t}, {var_therapist})) /// | |
/// extra block commands go here but omitted for clarity see below | |
mcmcs(2000) burnin(1000) | |
/// i've split these out, but some exta block commands to tell it to split the updates which are used in the model above | |
block({var_residual}, gibbs) /// | |
block({var_u}, gibbs) /// | |
block({var_therapist}, gibbs) /// | |
block({y:_cons}, gibbs) /// | |
block({y:i.id}, gibbs) /// | |
block({y:t}, gibbs) /// | |
block({y:i.therapist#c.t}, gibbs) /// |
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