benwhalley

mysim <- function(){
  des <- expand.grid(
    congruence=c(1,0),
    cuedresp=c("L", "R", "None"),
    trial=1:5,
    block=1:10,
    participant=1:20
  )
  samp <- des %>% rowwise() %>% mutate(y=rbinom(1,1,.03*congruence))

benwhalley

// 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)) ///

benwhalley

participant__username |         N
----------------------+----------
           Alex_Fowke |         5
        Carolyn_Hinds |         3
     Donna_Rutherford |         4
       Ellie_Campbell |         3
        Hannah_Wilson |         0
        Laura_Brummer |         5
          Sandy_Waite |         5
           Sara_Clark |         5

benwhalley

clear all
set seed 123

* recruit subjects (if only this easy...)
set obs 2000

* assign to groups
gen mbct =  mod(_n, 2)

* depression baseline score