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ragged.stan
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ragged.stan
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functions {
#include basis.stan
}
data {
int no_persons;
int no_measured_params;
array[no_persons] vector[no_measured_params] measured_params;
int no_experiments;
int total_no_measurements;
int person_idxs[no_experiments];
int measurement_idxs[no_experiments];
real alveolar_weights[no_experiments];
int no_measurements[no_experiments];
vector[no_experiments] exposures;
vector[no_experiments] exposure_times;
vector[total_no_measurements] measurement_times;
array[2] vector[total_no_measurements] measurements;
int no_latent_params;
vector<lower=0>[no_latent_params] population_eM_eM;
vector<lower=0>[no_latent_params] population_eM_eS;
vector<lower=1>[no_latent_params] population_eS_mu;
vector<lower=0>[no_latent_params] population_eS_nu;
real noise_scale;
real likelihood;
vector[total_no_measurements] sorted_measurement_times;
int active_no_measurements;
real dt;
int no_fit_sub_steps;
int no_sim_sub_steps;
vector[no_persons] centered_VPR;
vector[no_persons] centered_Fwp;
vector[no_persons] centered_Fpp;
vector[no_persons] centered_Ff;
vector[no_persons] centered_Fl;
vector[no_persons] centered_Vwp;
vector[no_persons] centered_Vpp;
vector[no_persons] centered_Vl;
vector[no_persons] centered_Pba;
vector[no_persons] centered_Pwp;
vector[no_persons] centered_Ppp;
vector[no_persons] centered_Pf;
vector[no_persons] centered_Pl;
vector[no_persons] centered_VMI;
vector[no_persons] centered_KMI;
int gq_no_experiments;
int qg_no_measurements;
vector[gq_no_experiments] gq_exposures;
vector[gq_no_experiments] gq_exposure_times;
vector[gq_no_experiments] gq_alveolar_weights;
vector[qg_no_measurements] gq_measurement_times;
}
transformed data {
int no_unconstrained_params = no_latent_params - 2;
array[no_persons] vector[no_latent_params] centered;
vector[no_latent_params] log_population_eM_eM = log(population_eM_eM);
vector[no_latent_params] log_population_eM_eS = log(population_eM_eS);
vector<lower=0>[no_latent_params] log_population_eS_mu = log(population_eS_mu);
vector[no_latent_params] log_log_population_eS_mu = log(log_population_eS_mu);
real active_time_threshold = sorted_measurement_times[active_no_measurements];
for(person in 1:no_persons){
centered[person] = [
centered_VPR[person],
centered_Fwp[person],
centered_Fpp[person],
centered_Ff[person],
centered_Fl[person],
centered_Vwp[person],
centered_Vpp[person],
centered_Vl[person],
centered_Pba[person],
centered_Pwp[person],
centered_Ppp[person],
centered_Pf[person],
centered_Pl[person],
centered_VMI[person],
centered_KMI[person]
]';
}
}
parameters {
vector[no_unconstrained_params] unit_log_population_eM;
vector[no_latent_params] unit_log_population_eS;
array[no_persons] vector[no_unconstrained_params] unit_log_person_params;
vector<lower=0>[2+max(measurement_idxs)] noise;
}
transformed parameters {
vector[no_latent_params] constrained_unit_log_population_eM = prepare_unit_log_vector(
unit_log_population_eM
);
vector[no_latent_params] log_population_eM = constrain_log_vector(
log_population_eM_eM + log_population_eM_eS .* constrained_unit_log_population_eM
);
vector<lower=0>[no_latent_params] population_eM = exp(log_population_eM);
vector<lower=0>[no_latent_params] log_population_eS = exp(
log_log_population_eS_mu + unit_log_population_eS
);
vector<lower=1>[no_latent_params] population_eS = exp(
log_population_eS
);
array[no_persons] vector[no_latent_params] constrained_unit_log_person_params;
array[no_persons] vector[no_latent_params] log_person_params;
array[no_persons] vector<lower=0>[no_latent_params] person_params;
real log_likelihood = 0;
population_eM[6:7] = (
.837 - population_eM[8]
) * population_eM[6:7];
for(person in 1:no_persons){
constrained_unit_log_person_params[person] = prepare_unit_log_vector(
unit_log_person_params[person]
);
log_person_params[person] = constrain_log_vector(
// log_population_eM +
(1 - centered[person]) .* log_population_eM +
pow(log_population_eS, 1 - centered[person]) .* constrained_unit_log_person_params[person]
);
person_params[person] = exp(log_person_params[person]);
person_params[person, 6:7] = (
.837 - person_params[person,8]
) * person_params[person, 6:7];
}
if(likelihood){
array[2] vector[total_no_measurements] fit_states;
int start_idx = 1;
for(experiment in 1:no_experiments){
int person = person_idxs[experiment];
int nidx = no_measurements[experiment];
int end_idx = start_idx;// + min(active_no_measurements, nidx) - 1;
// if(measurement_times[start_idx] > active_time_threshold){
// continue;
// }
for(idx in start_idx:start_idx+nidx-1){
if(measurement_times[idx] <= active_time_threshold){
end_idx = idx;
}else{
break;
}
}
fit_states[, start_idx:end_idx] = new_simulate_person(
exposures[experiment],
exposure_times[experiment],
measurement_times[start_idx:end_idx],
person_params[person], measured_params[person],
dt / no_fit_sub_steps,
alveolar_weights[experiment]
);
for(i in 1:2){
for(idx in start_idx:end_idx){
if(is_nan(measurements[i, idx])){
continue;
}
log_likelihood += lognormal_lpdf(
measurements[i, idx] |
log(fit_states[i, idx]),
noise[measurement_idxs[experiment] + i]
);
}
}
start_idx += nidx;
}
}
}
model {
target += normal_lpdf(constrained_unit_log_population_eM | 0,1);
target += scaled_inv_chi_square_lpdf(
pow(log_population_eS, 2) | population_eS_nu, log_population_eS_mu
);
target += log(pow(log_population_eS,2));
for(person in 1:no_persons){
target += normal_lpdf(
constrained_unit_log_person_params[person] |
// 0,
centered[person] .* pow(log_population_eS, centered[person] - 1) .* log_population_eM,
pow(log_population_eS, centered[person])
);
}
if(likelihood){
target += -log(noise);
}else{
target += weibull_lpdf(noise/noise_scale | 2,1);
}
target += log_likelihood;
}
generated quantities {
array[no_persons, gq_no_experiments, 2] vector[qg_no_measurements] gq_states;
for(person in 1:no_persons){
for(experiment in 1:gq_no_experiments){
gq_states[person, experiment] = new_simulate_person(
gq_exposures[experiment],
gq_exposure_times[experiment],
gq_measurement_times,
person_params[person], measured_params[person],
dt / no_sim_sub_steps,
gq_alveolar_weights[experiment]
);
}
}
}