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

run_factor

Code
  LNR_factor[[1]]$samples$theta_lambda[, , idx]
Output
                F1          F2
  m      0.4042440 0.000000000
  m_lMd -0.4318224 0.379480463
  s      0.6085597 0.101328100
  t0     0.1192645 0.006457266

Code
  LNR_factor[[1]]$samples$alpha[, , idx]
Output
              as1t       bd6t
  m     -0.9538521 -0.9353932
  m_lMd -0.3830433 -0.6787215
  s     -0.8030984 -0.4866918
  t0    -1.7952026 -1.6199259

Code
  LNR_factor[[1]]$samples$theta_mu[, idx]
Output
           m      m_lMd          s         t0 
  -0.5966560 -1.0288748 -0.2052301 -1.6511029

Code
  LNR_factor[[1]]$samples$theta_var[, , idx]
Output
                  m       m_lMd           s          t0
  m      0.19259767 -0.17456160  0.24600658  0.04821194
  m_lMd -0.17456160  0.35581441 -0.22433767 -0.04905066
  s      0.24600658 -0.22433767  0.45313739  0.07323385
  t0     0.04821194 -0.04905066  0.07323385  0.03879326

run_diag

Code
  LNR_diag[[1]]$samples$alpha[, , idx]
Output
              as1t       bd6t
  m     -0.8697654 -0.8967558
  m_lMd -0.3358620 -0.5448376
  s     -0.8933652 -0.5917299
  t0    -1.9745522 -1.6971386

Code
  LNR_diag[[1]]$samples$theta_mu[, idx]
Output
           m      m_lMd          s         t0 
  -0.8247087 -0.4798699 -0.6709188 -1.9290857

Code
  LNR_diag[[1]]$samples$theta_var[, , idx]
Output
                  m      m_lMd          s         t0
  m     0.001956766 0.00000000 0.00000000 0.00000000
  m_lMd 0.000000000 0.01533777 0.00000000 0.00000000
  s     0.000000000 0.00000000 0.03421786 0.00000000
  t0    0.000000000 0.00000000 0.00000000 0.01902554

run_blocked

Code
  LNR_blocked[[1]]$samples$alpha[, , idx]
Output
              as1t       bd6t
  m     -0.7045390 -0.9539209
  m_lMd -0.2739985 -0.6802633
  s     -1.1179201 -0.4835457
  t0    -2.7406658 -1.5598814

Code
  LNR_blocked[[1]]$samples$theta_mu[, idx]
Output
           m      m_lMd          s         t0 
  -0.7859169 -0.3300567  0.7986264 -0.6736691

Code
  LNR_blocked[[1]]$samples$theta_var[, , idx]
Output
                 m     m_lMd         s       t0
  m     0.04326159 0.0000000 0.0000000 0.000000
  m_lMd 0.00000000 0.2575511 0.0000000 0.000000
  s     0.00000000 0.0000000 0.9349592 1.020365
  t0    0.00000000 0.0000000 1.0203646 2.492648

run_single

Code
  LNR_single[[1]]$samples$alpha[, , idx]
Output
              as1t       bd6t
  m     -0.7546871 -0.9552423
  m_lMd -0.2646319 -0.6581083
  s     -0.8971468 -0.3139218
  t0    -2.2014758 -1.5023459

run_bridge

Code
  compare(list(single = LNR_single, diag = LNR_diag, factor = LNR_factor,
    blocked = LNR_blocked), stage = "preburn", cores_for_props = 1)
Output
            MD wMD  DIC wDIC BPIC wBPIC EffectiveN meanD Dmean minD
  single  -476   0 -313    1 -191     1        122  -436  -558 -558
  diag    -501   1    9    0  302     0        294  -285  -557 -579
  factor  -422   0 2584    0 4080     0       1496  1089  -392 -407
  blocked -152   0   60    0  343     0        284  -224  -478 -507