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Mar 26th, 2021
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  1. data{
  2. int count[128];
  3. vector[128] time;
  4. }
  5. parameters{
  6. real logK1_std;
  7. real<lower=0> r1_std;
  8. real<lower=0, upper = 1> L01_prop;
  9. real logK2_std;
  10. real<lower=0> neg_r2_std;
  11. real<lower=0, upper = 1> L02_prop;
  12. real<lower=0> theta_std;
  13. }
  14. transformed parameters{
  15. real<lower = 0> K1 = exp(log(10) + logK1_std * 3 * log(10));
  16. real<lower = 0> K2 = exp(log(10) + logK2_std * 3 * log(10));
  17. real<lower = 0> L01 = K1 * L01_prop;
  18. real<lower = 0> L02 = K2 * L02_prop;
  19. real<lower = 0> r1 = r1_std / 1;
  20. real<upper = 0> r2 = -neg_r2_std / 1;
  21. real<lower=0> theta = theta_std / 0.5;
  22. }
  23. model{
  24. vector[128] lambda;
  25. vector[128] alpha;
  26. vector[128] beta;
  27. theta_std ~ exponential( 1 );
  28.  
  29. //growth model
  30. r1_std ~ exponential( 1 );
  31. L01_prop ~ beta(1, 100);
  32. logK1_std ~ std_normal();
  33.  
  34. //decay model
  35. neg_r2_std ~ exponential( 1 );
  36. L02_prop ~ beta(100, 1);
  37. logK2_std ~ std_normal();
  38.  
  39. //mathemagically combine them
  40. for ( i in 1:128 ) {
  41. lambda[i] = K1 / (1 + ((K1 - L01) / L01) * exp(-r1 * time[i])) +
  42. K2 / (1 + ((K2 - L02) / L02) * exp(-r2 * time[i]));
  43. alpha[i] = lambda[i] / theta;
  44. beta[i] = 1 / theta;
  45. }
  46.  
  47. //evaluate likelihood
  48. count ~ neg_binomial( alpha , beta );
  49. }
  50. generated quantities{
  51. //none for now
  52. }
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