speaker-confusion-model/code/models/algo_siblings_adu.stan

functions {
    real recs_priors_lpmf(array[] int children,
        int start, int end,
        int n_recs,
        int n_classes,
        real recs_duration,
        array [] real age,
        matrix truth_vocs,
        vector mu_pop_level,
        matrix mu_child_level,
        vector alpha_child_level,
        vector child_dev_age,
        real beta_dev
        ) {
            real ll = 0;
            
            for (k in start:end) {
                real chi_mu = mu_pop_level[1]*exp(
                    child_dev_age[children[k-start+1]]*age[k]/12.0/10.0+beta_dev*(mu_child_level[children[k-start+1],2]+mu_child_level[children[k-start+1],3]-mu_pop_level[3]-mu_pop_level[4])*age[k]/12.0/10.0
                );
                ll += gamma_lpdf(
                    truth_vocs[k,1]/1000/recs_duration | alpha_child_level[1], alpha_child_level[1]/chi_mu
                );

                ll += gamma_lpdf(
                    truth_vocs[k,2:]/1000/recs_duration | alpha_child_level[2:], alpha_child_level[2:]./mu_child_level[children[k-start+1],:]' //'
                );
            }

            return ll;
        }
}

// TODO
// use speech rates to set priors on truth_vocs
data {
    int<lower=1> n_classes; // number of classes

    // analysis data block
    int<lower=1> n_recs;
    int<lower=1> n_children;

    array[n_recs] int<lower=1> children;
    array[n_recs] real<lower=1> age;
    array[n_recs] int<lower=-1> siblings;
    array[n_recs, n_classes] int<lower=0> vocs;
    array[n_children] int<lower=1> corpus;

    real<lower=0> recs_duration;

    int<lower=1> n_corpora;

    // actual speech rates
    int<lower=1> n_rates;
    int<lower=1> n_speech_rate_children;

    array [n_rates,n_classes] int<lower=0> speech_rates;
    array [n_rates] int group_corpus;
    array [n_rates] real<lower=0> durations;
    array [n_rates] real<lower=0> speech_rate_age;
    array [n_rates] int<lower=-1> speech_rate_siblings;
    array [n_rates] int<lower=1,upper=n_speech_rate_children> speech_rate_child;

    // parallel processing
    int<lower=1> threads;
}

transformed data {
    matrix[n_recs, n_classes] vocs_matrix = to_matrix(vocs);
    array[n_speech_rate_children] int<lower=1> speech_rate_child_corpus;

    array[n_children] int<lower=-1> child_siblings;
    array[n_speech_rate_children] int<lower=-1> speech_rate_child_siblings;
    int no_siblings = 0;
    int has_siblings = 0;

    for (k in 1:n_rates) {
        speech_rate_child_corpus[speech_rate_child[k]] = group_corpus[k];
    }

    for (k in 1:n_recs) {
        child_siblings[children[k]] = siblings[k];
    }

    for (c in 1:n_children) {
        if (child_siblings[c] == 0) {
            no_siblings += 1;
        }
        else if (child_siblings[c] > 0) {
            has_siblings += 1;
        }
    }

    for (k in 1:n_rates) {
        speech_rate_child_siblings[speech_rate_child[k]] = speech_rate_siblings[k];
    }
}

parameters {
    matrix<lower=0>[n_children,n_classes-1] mu_child_level;
    vector [n_children] child_dev_age;

    // speech rates
    vector<lower=0>[n_classes] alpha_child_level; // variance across recordings for a given child
    array[2] matrix<lower=0>[n_classes-1,n_corpora] alpha_corpus_level; // variance among children
    matrix<lower=0>[n_classes-1,n_corpora] mu_corpus_level; // child-level average
    vector<lower=0>[n_classes-1] alpha_pop_level; // variance among corpora
    vector<lower=0>[n_classes] mu_pop_level; // population level averages
    vector<lower=0>[n_classes-1] alpha_pop;
    matrix<lower=0>[n_classes,n_rates] speech_rate; // truth speech rates observed in annotated clips
    matrix<lower=0>[n_speech_rate_children,n_classes-1] speech_rate_child_level; // expected speech rate at the child-level

    // siblings
    real beta_sib_och; // effect of n of siblings on OCH speech
    real beta_sib_adu; // effect of n of siblings on ADU speech
    real<lower=0,upper=1> p_sib; // prob of having siblings

    vector [n_speech_rate_children] child_dev_speech_age;

    // average effect of age
    real alpha_dev;
    real<lower=0> sigma_dev;

    // effect of excess ADU input
    real beta_dev;
}

model {
    // priors on actual speech
    target += reduce_sum(
        recs_priors_lpmf, children, 1,
        n_recs, n_classes, recs_duration, age,
        vocs_matrix,
        mu_pop_level, mu_child_level, alpha_child_level,
        child_dev_age, beta_dev
    );

    vector [2] ll;
    int distrib;
    for (c in 1:n_children) {
        // if there is sibling data
        if (child_siblings[c]>=0) {
            distrib = child_siblings[c]>0?2:1;

            mu_child_level[c,1] ~ gamma(
                alpha_corpus_level[distrib,1,corpus[c]],
                (alpha_corpus_level[distrib,1,corpus[c]]/(mu_corpus_level[1,corpus[c]]*exp(
                   child_siblings[c]>0?beta_sib_och:0 
                )))
            );
            mu_child_level[c,2:] ~ gamma(
                alpha_corpus_level[distrib,2:,corpus[c]],
                (alpha_corpus_level[distrib,2:,corpus[c]]./mu_corpus_level[2:,corpus[c]]*exp(
                   child_siblings[c]>0?beta_sib_adu:0 
                ))
            );
        }
        // otherwise
        else {
            // assuming no sibling
            ll[1] = log(p_sib)+gamma_lpdf(
                mu_child_level[c,1] | alpha_corpus_level[2,1,corpus[c]], alpha_corpus_level[2,1,corpus[c]]/(mu_corpus_level[1,corpus[c]]*exp(beta_sib_och))
            );
            ll[1] += gamma_lpdf(
                mu_child_level[c,2] | alpha_corpus_level[2,2,corpus[c]], alpha_corpus_level[2,2,corpus[c]]/(mu_corpus_level[2,corpus[c]]*exp(beta_sib_adu))
            );
            ll[1] += gamma_lpdf(
                mu_child_level[c,3] | alpha_corpus_level[2,3,corpus[c]], alpha_corpus_level[2,3,corpus[c]]/(mu_corpus_level[3,corpus[c]]*exp(beta_sib_adu))
            );

            // assuming sibling
            ll[2] = log(1-p_sib)+gamma_lpdf(
                mu_child_level[c,1] | alpha_corpus_level[1,1,corpus[c]], alpha_corpus_level[1,1,corpus[c]]/(mu_corpus_level[1,corpus[c]])
            );
            ll[2] += gamma_lpdf(
                mu_child_level[c,2] | alpha_corpus_level[1,2,corpus[c]], alpha_corpus_level[1,2,corpus[c]]/(mu_corpus_level[2,corpus[c]])
            );
            ll[2] += gamma_lpdf(
                mu_child_level[c,3] | alpha_corpus_level[1,3,corpus[c]], alpha_corpus_level[1,3,corpus[c]]/(mu_corpus_level[3,corpus[c]])
            );
            target += log_sum_exp(ll);
        }
    }

    alpha_child_level ~ gamma(2,1);

    // speech rates
    mu_pop_level ~ exponential(4); // 250 vocs/hour
    alpha_pop_level ~ gamma(8, 4); // sd = 0.35 x \mu
    alpha_pop ~ gamma(10, 10);
    for (i in 1:n_classes-1) {
        alpha_corpus_level[1,i,:] ~ gamma(4, 4/alpha_pop[i]);
        alpha_corpus_level[2,i,:] ~ gamma(4, 4/alpha_pop[i]);
        mu_corpus_level[i,:] ~ gamma(alpha_pop_level[i],alpha_pop_level[i]/mu_pop_level[i+1]);
    }

    for (g in 1:n_rates) {
        real chi_mu = mu_pop_level[1]*exp(
            child_dev_speech_age[speech_rate_child[g]]*speech_rate_age[g]/12.0/10.0 + beta_dev*(speech_rate_child_level[speech_rate_child[g],2]+speech_rate_child_level[speech_rate_child[g],3]-mu_pop_level[3]-mu_pop_level[4])*speech_rate_age[g]/12.0/10.0
        );
        speech_rate[1,g] ~ gamma(
            alpha_child_level[1],
            alpha_child_level[1]/chi_mu
        );

        speech_rate[2:,g] ~ gamma(
            alpha_child_level[2:],
            (alpha_child_level[2:]./(speech_rate_child_level[speech_rate_child[g],:]')) //'
        );
        speech_rates[g,:] ~ poisson(speech_rate[:,g]*durations[g]*1000);
    }

    for (c in 1:n_speech_rate_children) {
        distrib = child_siblings[c]>0?2:1;

        speech_rate_child_level[c,1] ~ gamma(
            alpha_corpus_level[distrib,1,speech_rate_child_corpus[c]],
            (alpha_corpus_level[distrib,1,speech_rate_child_corpus[c]]/(mu_corpus_level[1,speech_rate_child_corpus[c]]*exp(
                speech_rate_child_siblings[c]>0?beta_sib_och:0
            )))
        );

        speech_rate_child_level[c,2:] ~ gamma(
            alpha_corpus_level[distrib,2:,speech_rate_child_corpus[c]],
            (alpha_corpus_level[distrib,2:,speech_rate_child_corpus[c]]./(mu_corpus_level[2:,speech_rate_child_corpus[c]]*exp(
                speech_rate_child_siblings[c]>0?beta_sib_adu:0
            )))
        );
    }

    child_dev_age ~ normal(alpha_dev, sigma_dev);
    child_dev_speech_age ~ normal(alpha_dev, sigma_dev);

    has_siblings ~ binomial(has_siblings+no_siblings, p_sib);
    p_sib ~ uniform(0, 1);
    beta_sib_och ~ normal(0, 1);
    beta_sib_adu ~ normal(0, 1);

    alpha_dev ~ normal(0, 1);
    sigma_dev ~ exponential(1);

    beta_dev ~ normal(0, 1);
}