map.md
In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

mapped_pars

Code
  mapped_pars(des)
Output
  $v 
    covariate           E         
   -0.513716908811104  speed     : v - 0.514 * v_covariate
   0.601813121611908   neutral   : v + 0.602 * v_covariate + v_Eneutral + 0.602 * v_covariate:Eneutral
   -1.54982186331112   accuracy  : v - 1.55 * v_covariate + v_Eaccuracy - 1.55 * v_covariate:Eaccuracy
   -1.70962282511647   speed     : v - 1.71 * v_covariate
   0.7701488026301     neutral   : v + 0.77 * v_covariate + v_Eneutral + 0.77 * v_covariate:Eneutral
   -0.716873043602934  accuracy  : v - 0.717 * v_covariate + v_Eaccuracy - 0.717 * v_covariate:Eaccuracy

  $B 
    E         
   speed     : exp(B)
   neutral   : exp(B + B_Eneutral)
   accuracy  : exp(B + B_Eaccuracy)

  $t0 
    S      
   left   : exp(t0)
   right  : exp(t0 + t0_Sright)

Code
  mapped_pars(des, p_vector = rnorm(length(sampled_pars(des))))
Output
            E     S   covariate    lR      v sv     B A    t0     b
  1     speed  left -0.29797771  left  1.637  1 3.048 0 0.292 3.048
  2     speed  left -0.29797771 right  1.637  1 3.048 0 0.292 3.048
  3   neutral  left  0.34063680  left  0.805  1 1.793 0 0.292 1.793
  4   neutral  left  0.34063680 right  0.805  1 1.793 0 0.292 1.793
  5  accuracy  left  0.13373072  left -0.213  1 3.051 0 0.292 3.051
  6  accuracy  left  0.13373072 right -0.213  1 3.051 0 0.292 3.051
  7     speed right  0.86266186  left  0.057  1 3.048 0 0.189 3.048
  8     speed right  0.86266186 right  0.057  1 3.048 0 0.189 3.048
  9   neutral right  0.05063779  left  0.895  1 1.793 0 0.189 1.793
  10  neutral right  0.05063779 right  0.895  1 1.793 0 0.189 1.793
  11 accuracy right  1.22458533  left -2.828  1 3.051 0 0.189 3.051
  12 accuracy right  1.22458533 right -2.828  1 3.051 0 0.189 3.051

Code
  mapped_pars(prior(des, mu_mean = c(v_covariate = 1)))
Output
            E     S   covariate    lR      v sv B A t0 b
  1     speed  left -0.09534976  left -0.095  1 1 0  1 1
  2     speed  left -0.09534976 right -0.095  1 1 0  1 1
  3   neutral  left -0.54328498  left -0.543  1 1 0  1 1
  4   neutral  left -0.54328498 right -0.543  1 1 0  1 1
  5  accuracy  left -2.15286166  left -2.153  1 1 0  1 1
  6  accuracy  left -2.15286166 right -2.153  1 1 0  1 1
  7     speed right -1.12658056  left -1.127  1 1 0  1 1
  8     speed right -1.12658056 right -1.127  1 1 0  1 1
  9   neutral right  0.67546458  left  0.675  1 1 0  1 1
  10  neutral right  0.67546458 right  0.675  1 1 0  1 1
  11 accuracy right -0.08025789  left -0.080  1 1 0  1 1
  12 accuracy right -0.08025789 right -0.080  1 1 0  1 1

Code
  mapped_pars(samples_LNR)
Output
            E     S    lR    lM      m     s    t0
  1     speed  left  left  TRUE -1.225 0.587 0.197
  2     speed  left right FALSE -0.711 0.587 0.197
  3   neutral  left  left  TRUE -1.225 0.587 0.197
  4   neutral  left right FALSE -0.711 0.587 0.197
  5  accuracy  left  left  TRUE -1.225 0.587 0.197
  6  accuracy  left right FALSE -0.711 0.587 0.197
  7     speed right  left FALSE -0.711 0.587 0.197
  8     speed right right  TRUE -1.225 0.587 0.197
  9   neutral right  left FALSE -0.711 0.587 0.197
  10  neutral right right  TRUE -1.225 0.587 0.197
  11 accuracy right  left FALSE -0.711 0.587 0.197
  12 accuracy right right  TRUE -1.225 0.587 0.197

Code
  mapped_pars(get_prior(samples_LNR))
Output
            E     S    lR    lM m s t0
  1     speed  left  left  TRUE 0 1  1
  2     speed  left right FALSE 0 1  1
  3   neutral  left  left  TRUE 0 1  1
  4   neutral  left right FALSE 0 1  1
  5  accuracy  left  left  TRUE 0 1  1
  6  accuracy  left right FALSE 0 1  1
  7     speed right  left FALSE 0 1  1
  8     speed right right  TRUE 0 1  1
  9   neutral right  left FALSE 0 1  1
  10  neutral right right  TRUE 0 1  1
  11 accuracy right  left FALSE 0 1  1
  12 accuracy right right  TRUE 0 1  1

Code
  mapped_pars(get_design(samples_LNR))
Output
  $m 
    lM     
   TRUE   : m + 0.5 * m_lMd
   FALSE  : m - 0.5 * m_lMd

map

Code
  credint(samples_LNR, selection = "mu", map = "E")
Output
  $mu
                 2.5%    50%  97.5%
  m_Eaccuracy  -1.008 -0.964 -0.919
  m_Eneutral   -1.008 -0.964 -0.919
  m_Espeed     -1.008 -0.964 -0.919
  s_Eaccuracy   0.554  0.583  0.619
  s_Eneutral    0.554  0.583  0.619
  s_Espeed      0.554  0.583  0.619
  t0_Eaccuracy  0.177  0.193  0.206
  t0_Eneutral   0.177  0.193  0.206
  t0_Espeed     0.177  0.193  0.206

Code
  credint(samples_LNR, selection = "mu", map = list(~ E * S))
Output
  $mu
                        2.5%    50%  97.5%
  m_(Intercept)       -1.008 -0.964 -0.919
  m_Eneutral          -1.008 -0.964 -0.919
  m_Eaccuracy         -1.008 -0.964 -0.919
  m_Sright            -1.008 -0.964 -0.919
  m_Eneutral:Sright   -1.008 -0.964 -0.919
  m_Eaccuracy:Sright  -1.008 -0.964 -0.919
  s_(Intercept)        0.554  0.583  0.619
  s_Eneutral           0.554  0.583  0.619
  s_Eaccuracy          0.554  0.583  0.619
  s_Sright             0.554  0.583  0.619
  s_Eneutral:Sright    0.554  0.583  0.619
  s_Eaccuracy:Sright   0.554  0.583  0.619
  t0_(Intercept)       0.177  0.193  0.206
  t0_Eneutral          0.177  0.193  0.206
  t0_Eaccuracy         0.177  0.193  0.206
  t0_Sright            0.177  0.193  0.206
  t0_Eneutral:Sright   0.177  0.193  0.206
  t0_Eaccuracy:Sright  0.177  0.193  0.206

Code
  credint(samples_LNR, selection = "mu", map = TRUE)
Output
  $mu
              2.5%    50%  97.5%
  m_lMFALSE -0.752 -0.700 -0.655
  m_lMTRUE  -1.271 -1.228 -1.167
  s          0.554  0.583  0.619
  t0         0.177  0.193  0.206