tests/testthat/_snaps/dino-code/param-estimates.md

In wjakethompson/measr: Bayesian Psychometric Measurement Using 'Stan'

dino model works

Code
  rstn_dino$stancode
Output
  data {
    int<lower=1> I;                      // number of items
    int<lower=1> R;                      // number of respondents
    int<lower=1> N;                      // number of observations
    int<lower=1> C;                      // number of classes
    int<lower=1> A;                      // number of attributes
    array[N] int<lower=1,upper=I> ii;    // item for observation n
    array[N] int<lower=1,upper=R> rr;    // respondent for observation n
    array[N] int<lower=0,upper=1> y;     // score for observation n
    array[R] int<lower=1,upper=N> start; // starting row for respondent R
    array[R] int<lower=1,upper=I> num;   // number of items for respondent R
    matrix[C,A] Alpha;                   // attribute pattern for each class
    matrix[I,C] Xi;                      // class attribute mastery indicator
  }
  parameters {
    simplex[C] Vc;                  // base rates of class membership

    ////////////////////////////////// item parameters
    array[I] real<lower=0,upper=1> slip;
    array[I] real<lower=0,upper=1> guess;
  }
  transformed parameters {
    vector[C] log_Vc = log(Vc);
    matrix[I,C] pi;

    for (i in 1:I) {
      for (c in 1:C) {
        pi[i,c] = ((1 - slip[i]) ^ Xi[i,c]) * (guess[i] ^ (1 - Xi[i,c]));
      }
    }
  }
  model {

    ////////////////////////////////// priors
    Vc ~ dirichlet(rep_vector(1, C));
    slip[1] ~ beta(5, 25);
    guess[1] ~ beta(5, 25);
    slip[2] ~ beta(5, 25);
    guess[2] ~ beta(5, 25);
    slip[3] ~ beta(5, 25);
    guess[3] ~ beta(5, 25);
    slip[4] ~ beta(5, 25);
    guess[4] ~ beta(5, 25);
    slip[5] ~ beta(5, 25);
    guess[5] ~ beta(5, 25);
    slip[6] ~ beta(5, 25);
    guess[6] ~ beta(5, 25);
    slip[7] ~ beta(5, 25);
    guess[7] ~ beta(5, 25);
    slip[8] ~ beta(5, 25);
    guess[8] ~ beta(5, 25);
    slip[9] ~ beta(5, 25);
    guess[9] ~ beta(5, 25);
    slip[10] ~ beta(5, 25);
    guess[10] ~ beta(5, 25);
    slip[11] ~ beta(5, 25);
    guess[11] ~ beta(5, 25);
    slip[12] ~ beta(5, 25);
    guess[12] ~ beta(5, 25);
    slip[13] ~ beta(5, 25);
    guess[13] ~ beta(5, 25);
    slip[14] ~ beta(5, 25);
    guess[14] ~ beta(5, 25);
    slip[15] ~ beta(5, 25);
    guess[15] ~ beta(5, 25);
    slip[16] ~ beta(5, 25);
    guess[16] ~ beta(5, 25);
    slip[17] ~ beta(5, 25);
    guess[17] ~ beta(5, 25);
    slip[18] ~ beta(5, 25);
    guess[18] ~ beta(5, 25);
    slip[19] ~ beta(5, 25);
    guess[19] ~ beta(5, 25);
    slip[20] ~ beta(5, 25);
    guess[20] ~ beta(5, 25);
    slip[21] ~ beta(5, 25);
    guess[21] ~ beta(5, 25);
    slip[22] ~ beta(5, 25);
    guess[22] ~ beta(5, 25);
    slip[23] ~ beta(5, 25);
    guess[23] ~ beta(5, 25);
    slip[24] ~ beta(5, 25);
    guess[24] ~ beta(5, 25);
    slip[25] ~ beta(5, 25);
    guess[25] ~ beta(5, 25);
    slip[26] ~ beta(5, 25);
    guess[26] ~ beta(5, 25);
    slip[27] ~ beta(5, 25);
    guess[27] ~ beta(5, 25);
    slip[28] ~ beta(5, 25);
    guess[28] ~ beta(5, 25);
    slip[29] ~ beta(5, 25);
    guess[29] ~ beta(5, 25);
    slip[30] ~ beta(5, 25);
    guess[30] ~ beta(5, 25);
    slip[31] ~ beta(5, 25);
    guess[31] ~ beta(5, 25);
    slip[32] ~ beta(5, 25);
    guess[32] ~ beta(5, 25);
    slip[33] ~ beta(5, 25);
    guess[33] ~ beta(5, 25);
    slip[34] ~ beta(5, 25);
    guess[34] ~ beta(5, 25);
    slip[35] ~ beta(5, 25);
    guess[35] ~ beta(5, 25);

    ////////////////////////////////// likelihood
    for (r in 1:R) {
      row_vector[C] ps;
      for (c in 1:C) {
        array[num[r]] real log_items;
        for (m in 1:num[r]) {
          int i = ii[start[r] + m - 1];
          log_items[m] = y[start[r] + m - 1] * log(pi[i,c]) +
                         (1 - y[start[r] + m - 1]) * log(1 - pi[i,c]);
        }
        ps[c] = log_Vc[c] + sum(log_items);
      }
      target += log_sum_exp(ps);
    }
  }

wjakethompson/measr documentation built on April 12, 2025, 9:46 p.m.