This a sampler interface to convert user-friendly data into the necessary format to feed the immunity estimation model.
Usage
sampling(
observations,
populations,
locations,
imugap_opts = imugap_options(),
stan_opts = stan_options()
)Arguments
- observations
a
[data.frame()], the observed data, with at least three columns:an
obs_idcolumn; any type, as long as unique, non-NAa
positivecolumn; non-negative integers, the observed number of vaccinated individualsa
sample_ncolumn; positive integers, the number of individuals sampled, must be greater than or equal to "positive"optionally, a
censoredcolumn; numeric, NA (uncensored) or 1 (right-censored); if not present, will be assumed NA
- populations
a
[data.frame()], the observation meta data, with columnsobs_id, any type; the observation the row concerns (i.e. id shared with an observations data object)loc_id, any type; the location the row concerns (i.e. id shared with a locations data object)dose, a non-zero, positive integer (1, 2, ...); what dose row concernscohort, a positive integer; the cohort at the location row concernsage, a positive integer; the age of that cohort row concernsweight, a numeric, (0, 1); the relative contribution of this row to an observation. Optional if each population row has a uniqueobs_id.
- locations
a
[data.frame()], with columnsloc_idandparent_id, of the same type. See Details for restrictions.- imugap_opts
options for the
imuGAPmodel- stan_opts
passed to
rstan::sampling(e.g.iter,chains).
Value
An object of class imugap_fit wrapping the raw stanfit object
along with settings and dataset metadata.
Examples
# \donttest{
data("locations_sim"); data("observations_sim"); data("populations_sim")
st_opts <- stan_options(chains = 2, iter = 500)
sampling(
observations_sim, populations_sim, locations_sim,
stan_opts = st_opts
)
#>
#> SAMPLING FOR MODEL 'impute_school_coverage_process_v6' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000215 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.15 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 500 [ 0%] (Warmup)
#> Chain 1: Iteration: 50 / 500 [ 10%] (Warmup)
#> Chain 1: Iteration: 100 / 500 [ 20%] (Warmup)
#> Chain 1: Iteration: 150 / 500 [ 30%] (Warmup)
#> Chain 1: Iteration: 200 / 500 [ 40%] (Warmup)
#> Chain 1: Iteration: 250 / 500 [ 50%] (Warmup)
#> Chain 1: Iteration: 251 / 500 [ 50%] (Sampling)
#> Chain 1: Iteration: 300 / 500 [ 60%] (Sampling)
#> Chain 1: Iteration: 350 / 500 [ 70%] (Sampling)
#> Chain 1: Iteration: 400 / 500 [ 80%] (Sampling)
#> Chain 1: Iteration: 450 / 500 [ 90%] (Sampling)
#> Chain 1: Iteration: 500 / 500 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 10.985 seconds (Warm-up)
#> Chain 1: 4.894 seconds (Sampling)
#> Chain 1: 15.879 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'impute_school_coverage_process_v6' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000194 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.94 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 500 [ 0%] (Warmup)
#> Chain 2: Iteration: 50 / 500 [ 10%] (Warmup)
#> Chain 2: Iteration: 100 / 500 [ 20%] (Warmup)
#> Chain 2: Iteration: 150 / 500 [ 30%] (Warmup)
#> Chain 2: Iteration: 200 / 500 [ 40%] (Warmup)
#> Chain 2: Iteration: 250 / 500 [ 50%] (Warmup)
#> Chain 2: Iteration: 251 / 500 [ 50%] (Sampling)
#> Chain 2: Iteration: 300 / 500 [ 60%] (Sampling)
#> Chain 2: Iteration: 350 / 500 [ 70%] (Sampling)
#> Chain 2: Iteration: 400 / 500 [ 80%] (Sampling)
#> Chain 2: Iteration: 450 / 500 [ 90%] (Sampling)
#> Chain 2: Iteration: 500 / 500 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 10.743 seconds (Warm-up)
#> Chain 2: 5.873 seconds (Sampling)
#> Chain 2: 16.616 seconds (Total)
#> Chain 2:
#> Warning: There were 6 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> $stanfit
#> Inference for Stan model: impute_school_coverage_process_v6.
#> 2 chains, each with iter=500; warmup=250; thin=1;
#> post-warmup draws per chain=250, total post-warmup draws=500.
#>
#> mean se_mean sd 2.5% 25% 50% 75%
#> beta_bs[1] -1.82 0.01 0.15 -2.11 -1.92 -1.82 -1.70
#> beta_bs[2] -1.73 0.01 0.12 -1.95 -1.81 -1.73 -1.65
#> beta_bs[3] -2.37 0.01 0.16 -2.65 -2.48 -2.36 -2.26
#> beta_bs[4] -3.07 0.01 0.18 -3.42 -3.20 -3.07 -2.95
#> beta_bs[5] -2.81 0.01 0.24 -3.30 -2.98 -2.80 -2.64
#> sigma_cnty 0.63 0.04 0.40 0.09 0.33 0.57 0.85
#> off_cnty[1] -0.30 0.02 0.26 -0.81 -0.46 -0.29 -0.12
#> off_cnty[2] -0.52 0.04 0.33 -1.22 -0.74 -0.52 -0.29
#> off_cnty[3] -0.31 0.03 0.33 -1.03 -0.51 -0.29 -0.06
#> sigma_sch 0.87 0.02 0.18 0.56 0.74 0.84 0.98
#> off_sch[1] -1.14 0.03 0.40 -2.03 -1.37 -1.13 -0.86
#> off_sch[2] -0.16 0.03 0.28 -0.71 -0.34 -0.17 0.01
#> off_sch[3] -0.96 0.03 0.39 -1.86 -1.19 -0.94 -0.70
#> off_sch[4] 0.32 0.02 0.34 -0.40 0.11 0.32 0.54
#> off_sch[5] 0.26 0.02 0.28 -0.27 0.08 0.25 0.45
#> off_sch[6] 0.38 0.03 0.27 -0.10 0.19 0.38 0.54
#> off_sch[7] 0.38 0.02 0.27 -0.09 0.19 0.37 0.54
#> off_sch[8] -1.18 0.04 0.49 -2.25 -1.42 -1.16 -0.86
#> off_sch[9] 0.83 0.02 0.26 0.36 0.66 0.83 0.99
#> off_sch[10] -0.07 0.02 0.28 -0.59 -0.25 -0.07 0.12
#> off_sch[11] 0.42 0.04 0.34 -0.21 0.17 0.43 0.65
#> off_sch[12] -1.02 0.04 0.50 -2.11 -1.32 -0.98 -0.70
#> off_sch[13] 0.61 0.04 0.35 -0.03 0.38 0.62 0.83
#> off_sch[14] -0.42 0.04 0.35 -1.05 -0.65 -0.40 -0.19
#> off_sch[15] 0.02 0.04 0.33 -0.58 -0.22 0.01 0.23
#> off_sch[16] 0.54 0.04 0.38 -0.16 0.29 0.54 0.80
#> off_sch[17] -1.86 0.04 0.60 -3.31 -2.21 -1.80 -1.43
#> off_sch[18] -1.26 0.03 0.41 -2.07 -1.53 -1.27 -0.99
#> off_sch[19] 0.37 0.03 0.33 -0.19 0.13 0.33 0.58
#> off_sch[20] 0.02 0.03 0.34 -0.55 -0.21 -0.02 0.22
#> off_sch[21] -1.00 0.04 0.50 -2.12 -1.29 -0.97 -0.69
#> off_sch[22] -0.09 0.03 0.42 -0.94 -0.37 -0.10 0.18
#> off_sch[23] 0.12 0.03 0.35 -0.50 -0.12 0.08 0.35
#> off_sch[24] 0.70 0.03 0.34 0.12 0.44 0.68 0.92
#> lambda_raw[1] 1.37 0.05 0.79 0.17 0.73 1.31 1.90
#> lambda_raw[2] 1.04 0.00 0.03 1.00 1.02 1.04 1.06
#> lp__ -1586.12 0.54 5.73 -1597.40 -1589.80 -1585.80 -1581.87
#> 97.5% n_eff Rhat
#> beta_bs[1] -1.55 268 1.00
#> beta_bs[2] -1.51 240 1.00
#> beta_bs[3] -2.07 257 1.00
#> beta_bs[4] -2.75 250 1.00
#> beta_bs[5] -2.37 368 1.00
#> sigma_cnty 1.62 91 1.01
#> off_cnty[1] 0.17 112 1.00
#> off_cnty[2] 0.10 81 1.01
#> off_cnty[3] 0.25 107 1.01
#> sigma_sch 1.28 145 1.03
#> off_sch[1] -0.42 204 1.00
#> off_sch[2] 0.45 123 1.00
#> off_sch[3] -0.24 170 1.01
#> off_sch[4] 0.98 192 1.00
#> off_sch[5] 0.86 136 1.00
#> off_sch[6] 0.91 109 1.00
#> off_sch[7] 0.90 128 1.00
#> off_sch[8] -0.41 153 1.01
#> off_sch[9] 1.37 115 1.00
#> off_sch[10] 0.53 126 1.00
#> off_sch[11] 1.10 89 1.01
#> off_sch[12] -0.14 195 1.00
#> off_sch[13] 1.32 85 1.02
#> off_sch[14] 0.25 87 1.01
#> off_sch[15] 0.68 87 1.01
#> off_sch[16] 1.27 91 1.01
#> off_sch[17] -0.90 211 1.00
#> off_sch[18] -0.46 160 1.01
#> off_sch[19] 1.10 109 1.01
#> off_sch[20] 0.77 125 1.01
#> off_sch[21] -0.01 190 1.00
#> off_sch[22] 0.77 186 1.01
#> off_sch[23] 0.84 124 1.01
#> off_sch[24] 1.47 112 1.01
#> lambda_raw[1] 3.25 225 1.00
#> lambda_raw[2] 1.11 214 1.00
#> lp__ -1576.30 112 1.00
#>
#> Samples were drawn using NUTS(diag_e) at Tue Jun 16 20:11:48 2026.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
#>
#> $settings
#> $settings$imugap_opts
#> $settings$imugap_opts$df
#> [1] 5
#>
#> $settings$imugap_opts$dose_schedule
#> [1] 1 4
#>
#> $settings$imugap_opts$object
#> S4 class stanmodel 'impute_school_coverage_process_v6' coded as follows:
#> functions {
#> // a function to convert lower bounds l_1, 1_2, ... 1_n
#> // to (lower, upper) pairs (l_1, l_2-1), (l_2, l_3-1), ...
#> array[,] int bounds_to_range(array[] int lowers, int ub) {
#> int size_bounds = size(lowers);
#> if (lowers[size_bounds] > ub) {
#> print("Upper bound, ", ub, " is less than last lower bound, ", lowers[size_bounds]);
#> }
#> array[size_bounds] int uppers;
#> for (i in 1:(size_bounds - 1)) {
#> uppers[i] = lowers[i+1] - 1;
#> }
#> uppers[size_bounds] = ub;
#> return { lowers, uppers };
#> }
#> // create a matrix, each column multiplied by corresponding row entry
#> matrix element_mult_expand(vector colv, row_vector rowv) {
#> int nrows = size(colv), ncols = size(rowv);
#> matrix[nrows, ncols] result;
#> for (i in 1:nrows) {
#> result[i,] = rowv * colv[i];
#> }
#> return result;
#> }
#> // Sequential diff
#> vector diff(vector obj) {
#> int sz = size(obj);
#> return obj[2:] - obj[:(sz-1)];
#> }
#> row_vector diff(row_vector obj) {
#> int sz = size(obj);
#> return obj[2:] - obj[:(sz-1)];
#> }
#> row_vector colsum(matrix obj) {
#> int ncols = cols(obj);
#> row_vector[ncols] res;
#> for (i in 1:ncols) {
#> res[i] = sum(obj[, i]);
#> }
#> return res;
#> }
#> vector rowsum(matrix obj) {
#> int nrows = rows(obj);
#> vector[nrows] res;
#> for (i in 1:nrows) {
#> res[i] = sum(obj[i, ]);
#> }
#> return res;
#> }
#> vector unrolled_dose(int n_yr, int n_doses, matrix dose_sched, vector lambda_raw, real epsilon_p) {
#> // assert: dose_sched is n_yr x n_doses
#> // assert: lambda_raw is n_doses x 1 (alt: would be n_doses x n_time)
#> vector[n_doses] lambda = exp(lambda_raw);
#> // the unconditional cdfs, given FoV + whatever remains; normalization factors
#> matrix[n_yr, n_doses] dXcdf, dXpdf, normdXpdf;
#> vector[n_doses] rem;
#> for (dose in 1:n_doses) {
#> dXcdf[, dose] = 1 - exp(-cumulative_sum(dose_sched[, dose] * lambda[dose]));
#> rem[dose] = 1 - dXcdf[n_yr, dose];
#> dXpdf[1, dose] = dXcdf[1, dose];
#> dXpdf[2:,dose] = diff(dXcdf[, dose]);
#> normdXpdf[, dose] = reverse(cumulative_sum(reverse(dXpdf[, dose]))) + rem[dose];
#> }
#> matrix[n_yr, n_doses] conditional_dXpdf = rep_matrix(0, n_yr, n_doses), conditional_dXcdf;
#> conditional_dXpdf[, 1] = dXpdf[, 1];
#> conditional_dXcdf[, 1] = dXcdf[, 1];
#> for (dose in 2:n_doses) {
#> int prev_dose = dose - 1;
#> // conditional probability => probability got dose n-1 at some time, then probability got dose n at later times
#> for (ly in 1:n_yr) {
#> if (normdXpdf[ly, dose] < epsilon_p) break; // if the remaining weight is negligible, avoid division by zero
#> conditional_dXpdf[ly:, dose] += conditional_dXpdf[ly, prev_dose] * dXpdf[ly:, dose] / normdXpdf[ly, dose];
#> }
#> conditional_dXcdf[, dose] = cumulative_sum(conditional_dXpdf[, dose]);
#> }
#> // TODO check unrolling of cdfs - should be dose 1 all years, then dose 2 all years, etc
#> vector[n_doses * n_yr] unrolled_dose_cdf = to_vector(conditional_dXcdf);
#> return unrolled_dose_cdf;
#> }
#> }
#> // need meta info:
#> // which cohorts, which places, which years of life
#> // => assume each measurement is independent, not progress of some matched individuals
#> data {
#> // #include data/shared_stateonly.stan
#> // STRUCTURAL DEFINITIONS
#> int<lower=1> n_yr; // number of years to model for each cohort - should be at least year of oldest observation
#> int<lower=1> n_cohort; // number of birth year cohorts
#> int<lower=1> n_sch; // number of schools
#>
#> // maps schools to county
#> int<lower=1> n_cnty;
#> array[n_cnty] int<lower=1, upper=n_sch> cnty_bounds; // which schools indices start each county
#> // dose schedules
#> int<lower=1> n_doses;
#> matrix<lower=0, upper=1>[n_yr, n_doses] dose_sched;
#> // DATA DEFINITIONS
#> int<lower=1> n_obs;
#> int<lower=0> y_obs[n_obs];
#> int<lower=0> y_smp[n_obs];
#> // have school id ranges for observations & for doses; school id 0 == statewide?
#> // int obs_sch_id_bounds[n_obs];
#> int<lower=n_obs> n_weights;
#> array[n_obs] int<lower=1, upper=n_weights> obs_to_weights_bounds; // each entry is the start of the range
#> int<lower=1,upper=n_sch + n_cnty + 1> weights_school[n_weights];
#> int<lower=1,upper=n_cohort> weights_cohort[n_weights];
#> int<lower=1,upper=n_yr> weights_life_year[n_weights];
#> int<lower=1,upper=n_doses> weights_dose[n_weights];
#> vector<lower=0,upper=1>[n_weights] weights; // contribution of this (school, cohort, year, dose) to an observation
#> // run mode: 0 = estimation, 1 = prediction
#> int<lower=0, upper=1> predict_mode;
#> // TODO: calculate these in stan?
#> // https://spinkney.github.io/helpful_stan_functions/group__splines.html
#> // state-level basis spline
#> int k_bs; // number of bspline basis functions
#> matrix[n_cohort, k_bs] bs; // basis functions
#> # observations may be right-censored
#> # observation data is assumed ordered uncensored, then right censored
#> # so n_uncensored_obs == n_obs, all observations are uncensored
#> # number of uncensored observations
#> int<lower = 0, upper = n_obs> n_uncensored_obs;
#> }
#> transformed data {
#> // #include transformed_data/stateonly.stan
#> // #include transformed_data/fixed_raw_lambda.stan
#> // #include transformed_data/fixed_logit_phi.stan
#> real epsilon_p = 1e-10;
#> // convert to lookup spans for convenience
#> array[2, n_cnty] int cnty_map = bounds_to_range(cnty_bounds, n_sch);
#> array[2, n_obs] int obs_map = bounds_to_range(obs_to_weights_bounds, n_weights);
#>
#> // Equivalent 1:n_cohort, for time trends
#> vector[n_cohort] cohort_shift_counter = linspaced_vector(n_cohort, 1, n_cohort);
#> int<lower=1> phi_lookup[n_weights];
#> int<lower=1> cdf_lookup[n_weights];
#> // because integer arrays don't support broadcasting ...
#> // unroll phi and cdf objects to support vectorization
#> for (weight_i in 1:n_weights) {
#> // phi ordered by school then cohort
#> phi_lookup[weight_i] = weights_cohort[weight_i] + (weights_school[weight_i] - 1) * n_cohort;
#> // ordered by dose then life year
#> cdf_lookup[weight_i] = weights_life_year[weight_i] + (weights_dose[weight_i] - 1) * n_yr;
#> }
#> int<lower=-1> y_obs_trans[n_obs];
#> for(i in 1:n_obs) {
#> y_obs_trans[i] = y_obs[i] - 1;
#> }
#> }
#> parameters {
#> // bases spline coeficcients
#> vector[k_bs] beta_bs; // spline betas
#> // #include parameters/constant_phi.stan
#> // #include parameters/cnty_sch_linear.stan
#> // offsets
#> real<lower=0> sigma_cnty;
#> row_vector<multiplier=sigma_cnty>[n_cnty] off_cnty;
#> real<lower=0> sigma_sch;
#> row_vector<multiplier=sigma_sch>[n_sch] off_sch;
#> // Vaccination uptake rate
#> vector[n_doses] lambda_raw;
#> }
#> model {
#> if (!predict_mode) {
#> // PRIORS - spline coefficients
#> beta_bs ~ normal(0, 10);
#> // PRIOR - lambda; relatively strong prior belief that ~95% coverage achieved in a year
#> // mean of 3 => 1 - exp(-3*1) == ~ 0.95
#> lambda_raw ~ normal(log(3), 1);
#> // Offsets - non-centered parameterization to speed up
#> sigma_cnty ~ cauchy(0, 1);
#> off_cnty ~ normal(0, sigma_cnty);
#> sigma_sch ~ cauchy(0, 2);
#> off_sch ~ normal(0, sigma_sch);
#> vector[n_obs] p_obs;
#> vector[n_cohort] logit_phi_st = bs * beta_bs;
#> row_vector[n_sch] shift = off_sch;
#> for (c in 1:n_cnty) {
#> shift[cnty_map[1,c]:cnty_map[2,c]] += off_cnty[c];
#> }
#> vector[(1+ n_cnty + n_sch) * n_cohort] phi = append_row(append_row(
#> inv_logit(logit_phi_st),
#> to_vector(inv_logit(
#> rep_matrix(logit_phi_st, n_cnty) + rep_matrix(off_cnty, n_cohort)
#> ))),
#> to_vector(inv_logit(
#> rep_matrix(logit_phi_st, n_sch) + rep_matrix(shift, n_cohort)
#> ))
#> );
#> vector[n_doses * n_yr] unrolled_dose_probs = unrolled_dose(n_yr, n_doses, dose_sched, lambda_raw, epsilon_p);
#> vector[n_weights] weighted = (1 - phi[phi_lookup]) .* unrolled_dose_probs[cdf_lookup] .* weights;
#> for (obs_i in 1:n_obs) {
#> p_obs[obs_i] = sum(weighted[obs_map[1,obs_i]:obs_map[2,obs_i]]);
#> }
#> if (n_uncensored_obs < n_obs) { # at least some censored observations
#> # p_s => 1 - p_s = p_f :: probability of at least this many successes =>
#> # probability of less than this many failures
#> if (n_uncensored_obs > 0) { # at least some uncensored observations
#> target += binomial_lpmf(y_obs[:n_uncensored_obs] | y_smp[:n_uncensored_obs], p_obs[:n_uncensored_obs]);
#> }
#> target += binomial_lcdf(y_obs[(n_uncensored_obs+1):] | y_smp[(n_uncensored_obs+1):], 1 - p_obs[(n_uncensored_obs+1):]);
#> } else { # all uncensored observations
#> y_obs ~ binomial(y_smp, p_obs); # vectorized
#> }
#> }
#> }
#> generated quantities {
#> vector[predict_mode ? n_obs : 0] p_obs;
#> if (predict_mode) {
#> vector[n_cohort] logit_phi_st = bs * beta_bs;
#> row_vector[n_sch] shift = off_sch;
#> for (c in 1:n_cnty) {
#> shift[cnty_map[1,c]:cnty_map[2,c]] += off_cnty[c];
#> }
#> vector[(1+ n_cnty + n_sch) * n_cohort] phi = append_row(append_row(
#> inv_logit(logit_phi_st),
#> to_vector(inv_logit(
#> rep_matrix(logit_phi_st, n_cnty) + rep_matrix(off_cnty, n_cohort)
#> ))),
#> to_vector(inv_logit(
#> rep_matrix(logit_phi_st, n_sch) + rep_matrix(shift, n_cohort)
#> ))
#> );
#> vector[n_doses * n_yr] unrolled_dose_probs = unrolled_dose(n_yr, n_doses, dose_sched, lambda_raw, epsilon_p);
#> vector[n_weights] weighted = (1 - phi[phi_lookup]) .* unrolled_dose_probs[cdf_lookup] .* weights;
#> for (obs_i in 1:n_obs) {
#> p_obs[obs_i] = sum(weighted[obs_map[1,obs_i]:obs_map[2,obs_i]]);
#> }
#> }
#> }
#> // This model represents vaccination as a discrete step, fixed hazard process
#> // therefore X(t) => X(t + deltaT) = X(t)*exp(-hazard*deltaT)
#> // which means the X-out flow == X(t)*(1-exp(-hazard*deltaT))
#> //
#> // for an arbitrary number of hazards & deltaTs, we simply sum:
#> // net outflow: X(t)*(1-exp(-sum(hazard*deltaT)))
#> //
#> // This model represents an overall hazard, shared across birth cohorts and
#> // vaccine doses. Additionally, birth cohorts have a propensity to vaccinate,
#> // which is fixed at birth and then applies throughout life.
#> //
#> // deltaT captures both time passage + eligibility. deltaT in the fitting is
#> // always == 1, but note that this is unitless - the data define what time means
#> // e.g. if birth cohorts are based on years, then deltaT is years, and a weight
#> // of 1 means eligible for a whole year, .25 eligible for a quarter, etc
#> //
#> // the hazard is constant for any given discrete time step, but can vary between
#> // time steps. the hazard time is denoted in absolute model time. however,
#> // cohort time is relative. so:
#> // hazard time == 1: one deltaT has passed, absolute time is 1
#> // cohort 1, time 1: one deltaT has passed => cohort 1 is 1 deltaT old,
#> // absolute time is also 1 (cohort 1 is the first cohort)
#> // cohort 2, time 2: two deltaT have passed => cohort 2 is 2 deltaT old, but
#> // cohort 1 is 3 deltaT old, and absolute time is 3
#> // generally:
#> // cohort n, time t: experienced t deltaTs; those deltaT corresponded to absolute
#> // times n, n+1, ..., n+t
#> //
#> // we assume each cohort has the same doses schedule, so the same deltaT weights
#> //
#> // so for cohort n, fraction of (non-phi/vaccinators) with 1+ doses at age 1:t:
#> // d1cdf = 1 - exp(-cumsum(weight[1:t] .* lambda[n:t]))
#> //
#> // and the incidence of new dose 1s is:
#> //
#> // c(d1cdf[1], diff(d1cdf))
#> //
#> // for subsequent doses, the hazard model is the same, but now is conditional on
#> // having received the first dose; thus the incremental hazard is still
#> // d2cdf = 1 - exp(-cumsum(weight2[1:t] .* lambda[n:t]))
#> // (note the different eligibility mask, weight2; assert weight2 strictly less than weight1)
#> // but only the fraction having dose 1 experience it.
#> // if we think about this in terms of newly-dose-1-in-deltaT-x:
#> // we can reframe in these terms:
#> // define d2pdf = c(d2cdf[1], diff(d2cdf)); pgtt = 1-d2cdf[t] (probably event has not occured by t)
#> // probability of dose 2 in year (x+1):t => d2pdf[(x+1):t]/sum(d2pdf[x+1:t] + pggt)
#> // we can then assign each incremental incidence of dose 1 in deltaT x to getting
#> // second dose in deltaT x+1:t (or not getting it by t)
#> //
#> // this can be built-up as a matrix once per cohort
#> // 1. calculate d2cdf, d2pdf, pgtt as above, for t == max cohort "age"
#> // 2. calculate remd2pdf = rev(cumsum(rev(d2pdf))) + pgtt
#> // 3. d2contrib =
#> // upper_triangle_1s * colwise element multiplication of d2pdf / rowwise remd2pdf * rowwise d2incidence => column sums => proportion in d2
#>
#>
#> $settings$stan_opts
#> $settings$stan_opts$chains
#> [1] 2
#>
#> $settings$stan_opts$iter
#> [1] 500
#>
#> $settings$stan_opts$data
#> $settings$stan_opts$data$n_uncensored_obs
#> [1] 638
#>
#> $settings$stan_opts$data$n_yr
#> [1] 18
#>
#> $settings$stan_opts$data$n_cohort
#> [1] 31
#>
#> $settings$stan_opts$data$n_sch
#> [1] 24
#>
#> $settings$stan_opts$data$n_doses
#> [1] 2
#>
#> $settings$stan_opts$data$dose_sched
#> [,1] [,2]
#> [1,] 0 0
#> [2,] 1 0
#> [3,] 1 0
#> [4,] 1 0
#> [5,] 1 1
#> [6,] 1 1
#> [7,] 1 1
#> [8,] 1 1
#> [9,] 1 1
#> [10,] 1 1
#> [11,] 1 1
#> [12,] 1 1
#> [13,] 1 1
#> [14,] 1 1
#> [15,] 1 1
#> [16,] 1 1
#> [17,] 1 1
#> [18,] 1 1
#>
#> $settings$stan_opts$data$k_bs
#> [1] 5
#>
#> $settings$stan_opts$data$bs
#> 1 2 3 4 5
#> [1,] 1.0000000000 0.000000e+00 0.00000000 0.000000e+00 0.0000000000
#> [2,] 0.8130370370 1.805185e-01 0.00637037 7.407407e-05 0.0000000000
#> [3,] 0.6509629630 3.241481e-01 0.02429630 5.925926e-04 0.0000000000
#> [4,] 0.5120000000 4.340000e-01 0.05200000 2.000000e-03 0.0000000000
#> [5,] 0.3943703704 5.131852e-01 0.08770370 4.740741e-03 0.0000000000
#> [6,] 0.2962962963 5.648148e-01 0.12962963 9.259259e-03 0.0000000000
#> [7,] 0.2160000000 5.920000e-01 0.17600000 1.600000e-02 0.0000000000
#> [8,] 0.1517037037 5.978519e-01 0.22503704 2.540741e-02 0.0000000000
#> [9,] 0.1016296296 5.854815e-01 0.27496296 3.792593e-02 0.0000000000
#> [10,] 0.0640000000 5.580000e-01 0.32400000 5.400000e-02 0.0000000000
#> [11,] 0.0370370370 5.185185e-01 0.37037037 7.407407e-02 0.0000000000
#> [12,] 0.0189629630 4.701481e-01 0.41229630 9.859259e-02 0.0000000000
#> [13,] 0.0080000000 4.160000e-01 0.44800000 1.280000e-01 0.0000000000
#> [14,] 0.0023703704 3.591852e-01 0.47570370 1.627407e-01 0.0000000000
#> [15,] 0.0002962963 3.028148e-01 0.49362963 2.032593e-01 0.0000000000
#> [16,] 0.0000000000 2.500000e-01 0.50000000 2.500000e-01 0.0000000000
#> [17,] 0.0000000000 2.032593e-01 0.49362963 3.028148e-01 0.0002962963
#> [18,] 0.0000000000 1.627407e-01 0.47570370 3.591852e-01 0.0023703704
#> [19,] 0.0000000000 1.280000e-01 0.44800000 4.160000e-01 0.0080000000
#> [20,] 0.0000000000 9.859259e-02 0.41229630 4.701481e-01 0.0189629630
#> [21,] 0.0000000000 7.407407e-02 0.37037037 5.185185e-01 0.0370370370
#> [22,] 0.0000000000 5.400000e-02 0.32400000 5.580000e-01 0.0640000000
#> [23,] 0.0000000000 3.792593e-02 0.27496296 5.854815e-01 0.1016296296
#> [24,] 0.0000000000 2.540741e-02 0.22503704 5.978519e-01 0.1517037037
#> [25,] 0.0000000000 1.600000e-02 0.17600000 5.920000e-01 0.2160000000
#> [26,] 0.0000000000 9.259259e-03 0.12962963 5.648148e-01 0.2962962963
#> [27,] 0.0000000000 4.740741e-03 0.08770370 5.131852e-01 0.3943703704
#> [28,] 0.0000000000 2.000000e-03 0.05200000 4.340000e-01 0.5120000000
#> [29,] 0.0000000000 5.925926e-04 0.02429630 3.241481e-01 0.6509629630
#> [30,] 0.0000000000 7.407407e-05 0.00637037 1.805185e-01 0.8130370370
#> [31,] 0.0000000000 0.000000e+00 0.00000000 0.000000e+00 1.0000000000
#> attr(,"degree")
#> [1] 3
#> attr(,"knots")
#> [1] 16
#> attr(,"Boundary.knots")
#> [1] 1 31
#> attr(,"intercept")
#> [1] TRUE
#> attr(,"class")
#> [1] "bs" "basis" "matrix"
#>
#> $settings$stan_opts$data$n_obs
#> [1] 698
#>
#> $settings$stan_opts$data$y_obs
#> [1] 16 14 14 10 8 7 6 9 5 4 4 4 4 4 6
#> [16] 6 8 12 10 14 11 10 13 16 19 43 44 42 42 35
#> [31] 42 41 43 51 48 44 43 44 46 54 56 54 57 57 54
#> [46] 57 54 52 54 48 59 63 58 67 64 62 58 56 60 65
#> [61] 65 65 64 67 62 64 67 64 61 63 62 57 65 60 60
#> [76] 69 79 76 82 88 83 87 84 91 87 97 93 101 103 98
#> [91] 96 97 103 102 106 112 105 111 112 108 71 73 73 85 83
#> [106] 87 82 82 79 77 79 80 75 83 85 88 86 90 91 92
#> [121] 89 93 96 101 108 58 59 58 45 44 48 52 52 49 47
#> [136] 52 59 54 54 55 54 55 55 54 52 57 57 53 52 50
#> [151] 50 51 48 53 51 48 46 52 52 55 54 50 45 47 49
#> [166] 49 50 53 53 62 48 53 49 47 47 46 50 49 54 54
#> [181] 50 54 57 58 57 61 64 58 57 56 61 63 63 62 64
#> [196] 59 59 65 65 55 283 288 278 279 280 279 290 290 308 309
#> [211] 290 325 297 318 305 321 318 320 338 337 340 319 330 333 332
#> [226] 68 61 65 72 68 73 77 73 71 66 66 70 72 71 71
#> [241] 79 82 76 78 75 80 78 68 79 79 61 57 63 64 61
#> [256] 52 58 58 63 63 64 67 67 71 75 77 79 81 76 80
#> [271] 83 84 82 90 83 34 32 30 35 33 36 33 37 35 36
#> [286] 34 34 35 37 35 35 36 34 37 32 36 33 34 34 39
#> [301] 64 63 71 75 72 76 74 79 77 82 71 76 81 87 86
#> [316] 81 82 85 83 90 92 92 94 84 87 72 64 62 62 60
#> [331] 59 64 59 67 65 60 65 63 65 69 73 75 75 71 72
#> [346] 78 80 83 85 80 21 20 24 21 21 20 13 17 20 19
#> [361] 19 19 15 15 18 13 14 13 15 16 23 24 22 26 30
#> [376] 95 111 113 110 114 110 112 116 112 119 118 119 123 117 121
#> [391] 113 118 114 115 111 101 106 100 98 100 239 242 240 236 239
#> [406] 239 247 238 241 241 253 246 244 245 240 238 256 233 256 250
#> [421] 250 255 266 256 259 12 20 15 20 25 19 14 14 10 13
#> [436] 11 11 17 18 19 16 21 20 20 17 13 10 14 11 8
#> [451] 282 280 276 261 273 271 263 276 275 265 282 286 283 287 279
#> [466] 290 287 287 288 288 291 301 285 291 298 53 54 54 51 44
#> [481] 47 46 41 44 41 40 36 43 41 45 45 42 47 48 47
#> [496] 46 51 48 55 60 42 42 44 44 36 30 32 30 30 34
#> [511] 32 35 41 37 44 40 46 50 47 47 48 45 49 53 51
#> [526] 86 83 81 90 84 96 104 92 89 96 98 98 102 97 104
#> [541] 102 101 98 96 96 105 97 107 103 101 77 74 78 75 78
#> [556] 75 81 77 79 84 73 79 79 72 75 77 79 78 85 82
#> [571] 86 85 92 91 87 76 63 67 56 60 58 63 64 60 61
#> [586] 68 67 66 67 72 77 68 74 68 71 69 71 70 71 65
#> [601] 1744 1719 1764 1754 1751 1766 1730 1778 1780 1819 1829 1823 1860 1872 1918
#> [616] 1898 1957 1970 1980 1991 2001 1964 2045 2003 1997 253 246 247 217 233
#> [631] 207 315 230 204 198 305 327 284 313 308 235 233 333 229 223
#> [646] 227 200 211 237 255 269 354 306 289 245 304 229 273 240 333
#> [661] 337 372 272 233 253 234 267 305 227 275 308 296 222 240 239
#> [676] 297 358 256 241 332 360 256 322 302 317 310 298 295 330 281
#> [691] 283 241 238 340 268 289 330 250
#>
#> $settings$stan_opts$data$y_smp
#> [1] 19 20 16 13 13 8 9 10 5 4 4 4 4 4 6
#> [16] 7 11 13 13 15 13 11 16 18 20 53 54 49 50 48
#> [31] 53 52 53 57 55 51 55 55 56 60 62 65 66 66 64
#> [46] 63 61 58 57 54 66 71 66 70 71 69 65 65 64 69
#> [61] 71 70 70 70 69 68 72 69 69 65 65 62 65 65 62
#> [76] 99 98 95 98 103 104 101 105 104 105 108 108 113 115 117
#> [91] 119 117 119 117 121 124 120 124 126 126 89 91 90 94 95
#> [106] 95 97 97 93 91 90 89 87 89 94 93 94 98 99 100
#> [121] 100 102 106 110 114 61 63 59 54 53 53 55 55 54 57
#> [136] 58 63 58 56 58 61 57 59 61 60 63 62 59 58 56
#> [151] 61 65 62 63 63 61 56 59 58 63 60 56 56 55 58
#> [166] 55 54 57 61 65 61 58 54 55 57 49 52 53 57 58
#> [181] 59 60 60 64 68 64 68 66 65 62 64 66 70 70 68
#> [196] 67 64 67 65 60 373 372 376 374 374 375 378 379 381 383
#> [211] 386 389 386 384 380 385 388 389 392 387 387 386 390 387 382
#> [226] 85 80 80 83 82 83 83 81 77 79 76 81 82 79 82
#> [241] 85 90 85 83 84 88 86 82 85 84 72 74 79 77 75
#> [256] 71 76 74 72 74 78 80 78 82 85 89 90 88 87 91
#> [271] 92 91 94 98 98 37 34 34 38 36 39 37 39 38 40
#> [286] 38 37 40 40 37 36 37 37 38 35 38 38 38 36 41
#> [301] 85 83 86 87 89 91 91 89 90 91 90 90 95 97 94
#> [316] 94 92 93 97 97 101 99 100 97 97 75 72 68 65 65
#> [331] 64 65 68 70 67 65 68 69 70 74 75 77 78 74 79
#> [346] 84 84 88 87 84 29 26 31 26 24 22 17 20 24 20
#> [361] 21 22 19 19 22 20 16 14 18 20 24 27 22 27 32
#> [376] 117 122 122 124 126 126 129 126 126 131 130 133 132 132 128
#> [391] 125 123 125 124 120 115 112 111 110 111 276 279 279 277 278
#> [406] 275 279 277 275 279 281 276 272 271 270 269 272 274 278 280
#> [421] 279 283 287 285 283 17 20 19 23 26 22 17 16 11 14
#> [436] 15 13 17 21 22 19 23 23 24 19 14 11 15 13 9
#> [451] 338 341 339 335 332 330 332 328 328 327 331 331 331 329 331
#> [466] 327 329 328 327 328 328 331 331 335 334 62 67 65 62 57
#> [481] 55 51 50 47 47 44 41 45 47 52 53 51 54 59 54
#> [496] 50 54 56 61 66 44 47 48 45 40 35 34 33 35 37
#> [511] 35 39 43 44 45 48 51 54 49 52 53 49 53 57 55
#> [526] 115 114 110 113 114 118 123 119 119 120 120 120 122 117 120
#> [541] 118 118 113 117 113 116 118 120 115 117 92 92 91 88 88
#> [556] 91 90 92 92 95 92 92 87 82 84 86 89 92 95 97
#> [571] 96 94 97 96 92 79 74 70 66 70 65 68 66 66 68
#> [586] 72 72 72 72 76 80 76 78 74 77 75 74 75 72 70
#> [601] 2154 2170 2148 2144 2142 2128 2128 2125 2115 2146 2142 2157 2159 2156 2183
#> [616] 2194 2212 2228 2243 2242 2246 2229 2257 2264 2254 285 280 290 250 255
#> [631] 235 345 265 230 215 340 345 310 415 400 311 289 436 290 296
#> [646] 292 251 259 303 329 344 432 363 355 307 367 267 333 279 386
#> [661] 396 433 318 276 297 276 328 374 282 343 378 368 268 291 294
#> [676] 357 435 303 281 394 418 289 363 342 367 339 337 330 362 311
#> [691] 313 269 273 385 292 325 374 301
#>
#> $settings$stan_opts$data$n_weights
#> [1] 750
#>
#> $settings$stan_opts$data$obs_to_weights_bounds
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
#> [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
#> [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
#> [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
#> [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
#> [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
#> [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
#> [253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270
#> [271] 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
#> [289] 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306
#> [307] 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324
#> [325] 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342
#> [343] 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360
#> [361] 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378
#> [379] 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396
#> [397] 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414
#> [415] 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
#> [433] 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450
#> [451] 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468
#> [469] 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486
#> [487] 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504
#> [505] 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522
#> [523] 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540
#> [541] 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558
#> [559] 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
#> [577] 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594
#> [595] 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612
#> [613] 613 614 615 616 617 618 619 620 621 622 623 624 625 626 631 636 641 646
#> [631] 651 656 661 666 671 676 681 686 691 692 693 694 695 696 697 698 699 700
#> [649] 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718
#> [667] 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
#> [685] 737 738 739 740 741 742 743 744 745 746 747 748 749 750
#>
#> $settings$stan_opts$data$weights_school
#> [1] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
#> [26] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
#> [51] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [76] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
#> [101] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
#> [126] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
#> [151] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
#> [176] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
#> [201] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
#> [226] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
#> [251] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
#> [276] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
#> [301] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
#> [326] 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21
#> [351] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
#> [376] 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
#> [401] 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
#> [426] 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26
#> [451] 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
#> [476] 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27
#> [501] 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
#> [526] 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28
#> [551] 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24
#> [576] 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
#> [601] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [626] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [651] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [676] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [701] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [726] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#> $settings$stan_opts$data$weights_cohort
#> [1] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [26] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [51] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [76] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [101] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [126] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [151] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [176] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [201] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [226] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [251] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [276] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [301] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [326] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [351] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [376] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [401] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [426] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [451] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [476] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [501] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [526] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [551] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [576] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [601] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [626] 3 4 5 6 7 4 5 6 7 8 5 6 7 8 9 6 7 8 9 10 7 8 9 10 11
#> [651] 8 9 10 11 12 9 10 11 12 13 10 11 12 13 14 11 12 13 14 15 12 13 14 15 16
#> [676] 13 14 15 16 17 14 15 16 17 18 15 16 17 18 19 2 3 4 5 6 7 8 9 10 11
#> [701] 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5
#> [726] 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
#>
#> $settings$stan_opts$data$weights_life_year
#> [1] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [26] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [51] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [76] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [101] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [126] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [151] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [176] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [201] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [226] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [251] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [276] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [301] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [326] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [351] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [376] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [401] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [426] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [451] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [476] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [501] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [526] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [551] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [576] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [601] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [626] 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14
#> [651] 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14
#> [676] 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14 2 2 2 2 2 2 2 2 2 2
#> [701] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3
#> [726] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>
#> $settings$stan_opts$data$weights_dose
#> [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [741] 1 1 1 1 1 1 1 1 1 1
#>
#> $settings$stan_opts$data$weights
#> [1] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [19] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [37] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [55] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [73] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [91] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [109] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [127] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [145] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [163] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [181] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [199] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [217] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [235] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [253] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [271] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [289] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [307] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [325] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [343] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [361] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [379] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [397] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [415] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [433] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [451] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [469] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [487] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [505] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [523] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [541] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [559] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [577] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [595] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [613] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.2 0.2 0.2 0.2 0.2
#> [631] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
#> [649] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
#> [667] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
#> [685] 0.2 0.2 0.2 0.2 0.2 0.2 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [703] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [721] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [739] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#>
#> $settings$stan_opts$data$n_cnty
#> [1] 3
#>
#> $settings$stan_opts$data$cnty_bounds
#> [1] 1 11 18
#>
#> $settings$stan_opts$data$predict_mode
#> [1] 0
#>
#>
#> $settings$stan_opts$object
#> S4 class stanmodel 'impute_school_coverage_process_v6' coded as follows:
#> functions {
#> // a function to convert lower bounds l_1, 1_2, ... 1_n
#> // to (lower, upper) pairs (l_1, l_2-1), (l_2, l_3-1), ...
#> array[,] int bounds_to_range(array[] int lowers, int ub) {
#> int size_bounds = size(lowers);
#> if (lowers[size_bounds] > ub) {
#> print("Upper bound, ", ub, " is less than last lower bound, ", lowers[size_bounds]);
#> }
#> array[size_bounds] int uppers;
#> for (i in 1:(size_bounds - 1)) {
#> uppers[i] = lowers[i+1] - 1;
#> }
#> uppers[size_bounds] = ub;
#> return { lowers, uppers };
#> }
#> // create a matrix, each column multiplied by corresponding row entry
#> matrix element_mult_expand(vector colv, row_vector rowv) {
#> int nrows = size(colv), ncols = size(rowv);
#> matrix[nrows, ncols] result;
#> for (i in 1:nrows) {
#> result[i,] = rowv * colv[i];
#> }
#> return result;
#> }
#> // Sequential diff
#> vector diff(vector obj) {
#> int sz = size(obj);
#> return obj[2:] - obj[:(sz-1)];
#> }
#> row_vector diff(row_vector obj) {
#> int sz = size(obj);
#> return obj[2:] - obj[:(sz-1)];
#> }
#> row_vector colsum(matrix obj) {
#> int ncols = cols(obj);
#> row_vector[ncols] res;
#> for (i in 1:ncols) {
#> res[i] = sum(obj[, i]);
#> }
#> return res;
#> }
#> vector rowsum(matrix obj) {
#> int nrows = rows(obj);
#> vector[nrows] res;
#> for (i in 1:nrows) {
#> res[i] = sum(obj[i, ]);
#> }
#> return res;
#> }
#> vector unrolled_dose(int n_yr, int n_doses, matrix dose_sched, vector lambda_raw, real epsilon_p) {
#> // assert: dose_sched is n_yr x n_doses
#> // assert: lambda_raw is n_doses x 1 (alt: would be n_doses x n_time)
#> vector[n_doses] lambda = exp(lambda_raw);
#> // the unconditional cdfs, given FoV + whatever remains; normalization factors
#> matrix[n_yr, n_doses] dXcdf, dXpdf, normdXpdf;
#> vector[n_doses] rem;
#> for (dose in 1:n_doses) {
#> dXcdf[, dose] = 1 - exp(-cumulative_sum(dose_sched[, dose] * lambda[dose]));
#> rem[dose] = 1 - dXcdf[n_yr, dose];
#> dXpdf[1, dose] = dXcdf[1, dose];
#> dXpdf[2:,dose] = diff(dXcdf[, dose]);
#> normdXpdf[, dose] = reverse(cumulative_sum(reverse(dXpdf[, dose]))) + rem[dose];
#> }
#> matrix[n_yr, n_doses] conditional_dXpdf = rep_matrix(0, n_yr, n_doses), conditional_dXcdf;
#> conditional_dXpdf[, 1] = dXpdf[, 1];
#> conditional_dXcdf[, 1] = dXcdf[, 1];
#> for (dose in 2:n_doses) {
#> int prev_dose = dose - 1;
#> // conditional probability => probability got dose n-1 at some time, then probability got dose n at later times
#> for (ly in 1:n_yr) {
#> if (normdXpdf[ly, dose] < epsilon_p) break; // if the remaining weight is negligible, avoid division by zero
#> conditional_dXpdf[ly:, dose] += conditional_dXpdf[ly, prev_dose] * dXpdf[ly:, dose] / normdXpdf[ly, dose];
#> }
#> conditional_dXcdf[, dose] = cumulative_sum(conditional_dXpdf[, dose]);
#> }
#> // TODO check unrolling of cdfs - should be dose 1 all years, then dose 2 all years, etc
#> vector[n_doses * n_yr] unrolled_dose_cdf = to_vector(conditional_dXcdf);
#> return unrolled_dose_cdf;
#> }
#> }
#> // need meta info:
#> // which cohorts, which places, which years of life
#> // => assume each measurement is independent, not progress of some matched individuals
#> data {
#> // #include data/shared_stateonly.stan
#> // STRUCTURAL DEFINITIONS
#> int<lower=1> n_yr; // number of years to model for each cohort - should be at least year of oldest observation
#> int<lower=1> n_cohort; // number of birth year cohorts
#> int<lower=1> n_sch; // number of schools
#>
#> // maps schools to county
#> int<lower=1> n_cnty;
#> array[n_cnty] int<lower=1, upper=n_sch> cnty_bounds; // which schools indices start each county
#> // dose schedules
#> int<lower=1> n_doses;
#> matrix<lower=0, upper=1>[n_yr, n_doses] dose_sched;
#> // DATA DEFINITIONS
#> int<lower=1> n_obs;
#> int<lower=0> y_obs[n_obs];
#> int<lower=0> y_smp[n_obs];
#> // have school id ranges for observations & for doses; school id 0 == statewide?
#> // int obs_sch_id_bounds[n_obs];
#> int<lower=n_obs> n_weights;
#> array[n_obs] int<lower=1, upper=n_weights> obs_to_weights_bounds; // each entry is the start of the range
#> int<lower=1,upper=n_sch + n_cnty + 1> weights_school[n_weights];
#> int<lower=1,upper=n_cohort> weights_cohort[n_weights];
#> int<lower=1,upper=n_yr> weights_life_year[n_weights];
#> int<lower=1,upper=n_doses> weights_dose[n_weights];
#> vector<lower=0,upper=1>[n_weights] weights; // contribution of this (school, cohort, year, dose) to an observation
#> // run mode: 0 = estimation, 1 = prediction
#> int<lower=0, upper=1> predict_mode;
#> // TODO: calculate these in stan?
#> // https://spinkney.github.io/helpful_stan_functions/group__splines.html
#> // state-level basis spline
#> int k_bs; // number of bspline basis functions
#> matrix[n_cohort, k_bs] bs; // basis functions
#> # observations may be right-censored
#> # observation data is assumed ordered uncensored, then right censored
#> # so n_uncensored_obs == n_obs, all observations are uncensored
#> # number of uncensored observations
#> int<lower = 0, upper = n_obs> n_uncensored_obs;
#> }
#> transformed data {
#> // #include transformed_data/stateonly.stan
#> // #include transformed_data/fixed_raw_lambda.stan
#> // #include transformed_data/fixed_logit_phi.stan
#> real epsilon_p = 1e-10;
#> // convert to lookup spans for convenience
#> array[2, n_cnty] int cnty_map = bounds_to_range(cnty_bounds, n_sch);
#> array[2, n_obs] int obs_map = bounds_to_range(obs_to_weights_bounds, n_weights);
#>
#> // Equivalent 1:n_cohort, for time trends
#> vector[n_cohort] cohort_shift_counter = linspaced_vector(n_cohort, 1, n_cohort);
#> int<lower=1> phi_lookup[n_weights];
#> int<lower=1> cdf_lookup[n_weights];
#> // because integer arrays don't support broadcasting ...
#> // unroll phi and cdf objects to support vectorization
#> for (weight_i in 1:n_weights) {
#> // phi ordered by school then cohort
#> phi_lookup[weight_i] = weights_cohort[weight_i] + (weights_school[weight_i] - 1) * n_cohort;
#> // ordered by dose then life year
#> cdf_lookup[weight_i] = weights_life_year[weight_i] + (weights_dose[weight_i] - 1) * n_yr;
#> }
#> int<lower=-1> y_obs_trans[n_obs];
#> for(i in 1:n_obs) {
#> y_obs_trans[i] = y_obs[i] - 1;
#> }
#> }
#> parameters {
#> // bases spline coeficcients
#> vector[k_bs] beta_bs; // spline betas
#> // #include parameters/constant_phi.stan
#> // #include parameters/cnty_sch_linear.stan
#> // offsets
#> real<lower=0> sigma_cnty;
#> row_vector<multiplier=sigma_cnty>[n_cnty] off_cnty;
#> real<lower=0> sigma_sch;
#> row_vector<multiplier=sigma_sch>[n_sch] off_sch;
#> // Vaccination uptake rate
#> vector[n_doses] lambda_raw;
#> }
#> model {
#> if (!predict_mode) {
#> // PRIORS - spline coefficients
#> beta_bs ~ normal(0, 10);
#> // PRIOR - lambda; relatively strong prior belief that ~95% coverage achieved in a year
#> // mean of 3 => 1 - exp(-3*1) == ~ 0.95
#> lambda_raw ~ normal(log(3), 1);
#> // Offsets - non-centered parameterization to speed up
#> sigma_cnty ~ cauchy(0, 1);
#> off_cnty ~ normal(0, sigma_cnty);
#> sigma_sch ~ cauchy(0, 2);
#> off_sch ~ normal(0, sigma_sch);
#> vector[n_obs] p_obs;
#> vector[n_cohort] logit_phi_st = bs * beta_bs;
#> row_vector[n_sch] shift = off_sch;
#> for (c in 1:n_cnty) {
#> shift[cnty_map[1,c]:cnty_map[2,c]] += off_cnty[c];
#> }
#> vector[(1+ n_cnty + n_sch) * n_cohort] phi = append_row(append_row(
#> inv_logit(logit_phi_st),
#> to_vector(inv_logit(
#> rep_matrix(logit_phi_st, n_cnty) + rep_matrix(off_cnty, n_cohort)
#> ))),
#> to_vector(inv_logit(
#> rep_matrix(logit_phi_st, n_sch) + rep_matrix(shift, n_cohort)
#> ))
#> );
#> vector[n_doses * n_yr] unrolled_dose_probs = unrolled_dose(n_yr, n_doses, dose_sched, lambda_raw, epsilon_p);
#> vector[n_weights] weighted = (1 - phi[phi_lookup]) .* unrolled_dose_probs[cdf_lookup] .* weights;
#> for (obs_i in 1:n_obs) {
#> p_obs[obs_i] = sum(weighted[obs_map[1,obs_i]:obs_map[2,obs_i]]);
#> }
#> if (n_uncensored_obs < n_obs) { # at least some censored observations
#> # p_s => 1 - p_s = p_f :: probability of at least this many successes =>
#> # probability of less than this many failures
#> if (n_uncensored_obs > 0) { # at least some uncensored observations
#> target += binomial_lpmf(y_obs[:n_uncensored_obs] | y_smp[:n_uncensored_obs], p_obs[:n_uncensored_obs]);
#> }
#> target += binomial_lcdf(y_obs[(n_uncensored_obs+1):] | y_smp[(n_uncensored_obs+1):], 1 - p_obs[(n_uncensored_obs+1):]);
#> } else { # all uncensored observations
#> y_obs ~ binomial(y_smp, p_obs); # vectorized
#> }
#> }
#> }
#> generated quantities {
#> vector[predict_mode ? n_obs : 0] p_obs;
#> if (predict_mode) {
#> vector[n_cohort] logit_phi_st = bs * beta_bs;
#> row_vector[n_sch] shift = off_sch;
#> for (c in 1:n_cnty) {
#> shift[cnty_map[1,c]:cnty_map[2,c]] += off_cnty[c];
#> }
#> vector[(1+ n_cnty + n_sch) * n_cohort] phi = append_row(append_row(
#> inv_logit(logit_phi_st),
#> to_vector(inv_logit(
#> rep_matrix(logit_phi_st, n_cnty) + rep_matrix(off_cnty, n_cohort)
#> ))),
#> to_vector(inv_logit(
#> rep_matrix(logit_phi_st, n_sch) + rep_matrix(shift, n_cohort)
#> ))
#> );
#> vector[n_doses * n_yr] unrolled_dose_probs = unrolled_dose(n_yr, n_doses, dose_sched, lambda_raw, epsilon_p);
#> vector[n_weights] weighted = (1 - phi[phi_lookup]) .* unrolled_dose_probs[cdf_lookup] .* weights;
#> for (obs_i in 1:n_obs) {
#> p_obs[obs_i] = sum(weighted[obs_map[1,obs_i]:obs_map[2,obs_i]]);
#> }
#> }
#> }
#> // This model represents vaccination as a discrete step, fixed hazard process
#> // therefore X(t) => X(t + deltaT) = X(t)*exp(-hazard*deltaT)
#> // which means the X-out flow == X(t)*(1-exp(-hazard*deltaT))
#> //
#> // for an arbitrary number of hazards & deltaTs, we simply sum:
#> // net outflow: X(t)*(1-exp(-sum(hazard*deltaT)))
#> //
#> // This model represents an overall hazard, shared across birth cohorts and
#> // vaccine doses. Additionally, birth cohorts have a propensity to vaccinate,
#> // which is fixed at birth and then applies throughout life.
#> //
#> // deltaT captures both time passage + eligibility. deltaT in the fitting is
#> // always == 1, but note that this is unitless - the data define what time means
#> // e.g. if birth cohorts are based on years, then deltaT is years, and a weight
#> // of 1 means eligible for a whole year, .25 eligible for a quarter, etc
#> //
#> // the hazard is constant for any given discrete time step, but can vary between
#> // time steps. the hazard time is denoted in absolute model time. however,
#> // cohort time is relative. so:
#> // hazard time == 1: one deltaT has passed, absolute time is 1
#> // cohort 1, time 1: one deltaT has passed => cohort 1 is 1 deltaT old,
#> // absolute time is also 1 (cohort 1 is the first cohort)
#> // cohort 2, time 2: two deltaT have passed => cohort 2 is 2 deltaT old, but
#> // cohort 1 is 3 deltaT old, and absolute time is 3
#> // generally:
#> // cohort n, time t: experienced t deltaTs; those deltaT corresponded to absolute
#> // times n, n+1, ..., n+t
#> //
#> // we assume each cohort has the same doses schedule, so the same deltaT weights
#> //
#> // so for cohort n, fraction of (non-phi/vaccinators) with 1+ doses at age 1:t:
#> // d1cdf = 1 - exp(-cumsum(weight[1:t] .* lambda[n:t]))
#> //
#> // and the incidence of new dose 1s is:
#> //
#> // c(d1cdf[1], diff(d1cdf))
#> //
#> // for subsequent doses, the hazard model is the same, but now is conditional on
#> // having received the first dose; thus the incremental hazard is still
#> // d2cdf = 1 - exp(-cumsum(weight2[1:t] .* lambda[n:t]))
#> // (note the different eligibility mask, weight2; assert weight2 strictly less than weight1)
#> // but only the fraction having dose 1 experience it.
#> // if we think about this in terms of newly-dose-1-in-deltaT-x:
#> // we can reframe in these terms:
#> // define d2pdf = c(d2cdf[1], diff(d2cdf)); pgtt = 1-d2cdf[t] (probably event has not occured by t)
#> // probability of dose 2 in year (x+1):t => d2pdf[(x+1):t]/sum(d2pdf[x+1:t] + pggt)
#> // we can then assign each incremental incidence of dose 1 in deltaT x to getting
#> // second dose in deltaT x+1:t (or not getting it by t)
#> //
#> // this can be built-up as a matrix once per cohort
#> // 1. calculate d2cdf, d2pdf, pgtt as above, for t == max cohort "age"
#> // 2. calculate remd2pdf = rev(cumsum(rev(d2pdf))) + pgtt
#> // 3. d2contrib =
#> // upper_triangle_1s * colwise element multiplication of d2pdf / rowwise remd2pdf * rowwise d2incidence => column sums => proportion in d2
#>
#>
#>
#> $data
#> $data$n_uncensored_obs
#> [1] 638
#>
#> $data$n_yr
#> [1] 18
#>
#> $data$n_cohort
#> [1] 31
#>
#> $data$n_sch
#> [1] 24
#>
#> $data$n_doses
#> [1] 2
#>
#> $data$dose_sched
#> [,1] [,2]
#> [1,] 0 0
#> [2,] 1 0
#> [3,] 1 0
#> [4,] 1 0
#> [5,] 1 1
#> [6,] 1 1
#> [7,] 1 1
#> [8,] 1 1
#> [9,] 1 1
#> [10,] 1 1
#> [11,] 1 1
#> [12,] 1 1
#> [13,] 1 1
#> [14,] 1 1
#> [15,] 1 1
#> [16,] 1 1
#> [17,] 1 1
#> [18,] 1 1
#>
#> $data$k_bs
#> [1] 5
#>
#> $data$bs
#> 1 2 3 4 5
#> [1,] 1.0000000000 0.000000e+00 0.00000000 0.000000e+00 0.0000000000
#> [2,] 0.8130370370 1.805185e-01 0.00637037 7.407407e-05 0.0000000000
#> [3,] 0.6509629630 3.241481e-01 0.02429630 5.925926e-04 0.0000000000
#> [4,] 0.5120000000 4.340000e-01 0.05200000 2.000000e-03 0.0000000000
#> [5,] 0.3943703704 5.131852e-01 0.08770370 4.740741e-03 0.0000000000
#> [6,] 0.2962962963 5.648148e-01 0.12962963 9.259259e-03 0.0000000000
#> [7,] 0.2160000000 5.920000e-01 0.17600000 1.600000e-02 0.0000000000
#> [8,] 0.1517037037 5.978519e-01 0.22503704 2.540741e-02 0.0000000000
#> [9,] 0.1016296296 5.854815e-01 0.27496296 3.792593e-02 0.0000000000
#> [10,] 0.0640000000 5.580000e-01 0.32400000 5.400000e-02 0.0000000000
#> [11,] 0.0370370370 5.185185e-01 0.37037037 7.407407e-02 0.0000000000
#> [12,] 0.0189629630 4.701481e-01 0.41229630 9.859259e-02 0.0000000000
#> [13,] 0.0080000000 4.160000e-01 0.44800000 1.280000e-01 0.0000000000
#> [14,] 0.0023703704 3.591852e-01 0.47570370 1.627407e-01 0.0000000000
#> [15,] 0.0002962963 3.028148e-01 0.49362963 2.032593e-01 0.0000000000
#> [16,] 0.0000000000 2.500000e-01 0.50000000 2.500000e-01 0.0000000000
#> [17,] 0.0000000000 2.032593e-01 0.49362963 3.028148e-01 0.0002962963
#> [18,] 0.0000000000 1.627407e-01 0.47570370 3.591852e-01 0.0023703704
#> [19,] 0.0000000000 1.280000e-01 0.44800000 4.160000e-01 0.0080000000
#> [20,] 0.0000000000 9.859259e-02 0.41229630 4.701481e-01 0.0189629630
#> [21,] 0.0000000000 7.407407e-02 0.37037037 5.185185e-01 0.0370370370
#> [22,] 0.0000000000 5.400000e-02 0.32400000 5.580000e-01 0.0640000000
#> [23,] 0.0000000000 3.792593e-02 0.27496296 5.854815e-01 0.1016296296
#> [24,] 0.0000000000 2.540741e-02 0.22503704 5.978519e-01 0.1517037037
#> [25,] 0.0000000000 1.600000e-02 0.17600000 5.920000e-01 0.2160000000
#> [26,] 0.0000000000 9.259259e-03 0.12962963 5.648148e-01 0.2962962963
#> [27,] 0.0000000000 4.740741e-03 0.08770370 5.131852e-01 0.3943703704
#> [28,] 0.0000000000 2.000000e-03 0.05200000 4.340000e-01 0.5120000000
#> [29,] 0.0000000000 5.925926e-04 0.02429630 3.241481e-01 0.6509629630
#> [30,] 0.0000000000 7.407407e-05 0.00637037 1.805185e-01 0.8130370370
#> [31,] 0.0000000000 0.000000e+00 0.00000000 0.000000e+00 1.0000000000
#> attr(,"degree")
#> [1] 3
#> attr(,"knots")
#> [1] 16
#> attr(,"Boundary.knots")
#> [1] 1 31
#> attr(,"intercept")
#> [1] TRUE
#> attr(,"class")
#> [1] "bs" "basis" "matrix"
#>
#> $data$n_obs
#> [1] 698
#>
#> $data$y_obs
#> [1] 16 14 14 10 8 7 6 9 5 4 4 4 4 4 6
#> [16] 6 8 12 10 14 11 10 13 16 19 43 44 42 42 35
#> [31] 42 41 43 51 48 44 43 44 46 54 56 54 57 57 54
#> [46] 57 54 52 54 48 59 63 58 67 64 62 58 56 60 65
#> [61] 65 65 64 67 62 64 67 64 61 63 62 57 65 60 60
#> [76] 69 79 76 82 88 83 87 84 91 87 97 93 101 103 98
#> [91] 96 97 103 102 106 112 105 111 112 108 71 73 73 85 83
#> [106] 87 82 82 79 77 79 80 75 83 85 88 86 90 91 92
#> [121] 89 93 96 101 108 58 59 58 45 44 48 52 52 49 47
#> [136] 52 59 54 54 55 54 55 55 54 52 57 57 53 52 50
#> [151] 50 51 48 53 51 48 46 52 52 55 54 50 45 47 49
#> [166] 49 50 53 53 62 48 53 49 47 47 46 50 49 54 54
#> [181] 50 54 57 58 57 61 64 58 57 56 61 63 63 62 64
#> [196] 59 59 65 65 55 283 288 278 279 280 279 290 290 308 309
#> [211] 290 325 297 318 305 321 318 320 338 337 340 319 330 333 332
#> [226] 68 61 65 72 68 73 77 73 71 66 66 70 72 71 71
#> [241] 79 82 76 78 75 80 78 68 79 79 61 57 63 64 61
#> [256] 52 58 58 63 63 64 67 67 71 75 77 79 81 76 80
#> [271] 83 84 82 90 83 34 32 30 35 33 36 33 37 35 36
#> [286] 34 34 35 37 35 35 36 34 37 32 36 33 34 34 39
#> [301] 64 63 71 75 72 76 74 79 77 82 71 76 81 87 86
#> [316] 81 82 85 83 90 92 92 94 84 87 72 64 62 62 60
#> [331] 59 64 59 67 65 60 65 63 65 69 73 75 75 71 72
#> [346] 78 80 83 85 80 21 20 24 21 21 20 13 17 20 19
#> [361] 19 19 15 15 18 13 14 13 15 16 23 24 22 26 30
#> [376] 95 111 113 110 114 110 112 116 112 119 118 119 123 117 121
#> [391] 113 118 114 115 111 101 106 100 98 100 239 242 240 236 239
#> [406] 239 247 238 241 241 253 246 244 245 240 238 256 233 256 250
#> [421] 250 255 266 256 259 12 20 15 20 25 19 14 14 10 13
#> [436] 11 11 17 18 19 16 21 20 20 17 13 10 14 11 8
#> [451] 282 280 276 261 273 271 263 276 275 265 282 286 283 287 279
#> [466] 290 287 287 288 288 291 301 285 291 298 53 54 54 51 44
#> [481] 47 46 41 44 41 40 36 43 41 45 45 42 47 48 47
#> [496] 46 51 48 55 60 42 42 44 44 36 30 32 30 30 34
#> [511] 32 35 41 37 44 40 46 50 47 47 48 45 49 53 51
#> [526] 86 83 81 90 84 96 104 92 89 96 98 98 102 97 104
#> [541] 102 101 98 96 96 105 97 107 103 101 77 74 78 75 78
#> [556] 75 81 77 79 84 73 79 79 72 75 77 79 78 85 82
#> [571] 86 85 92 91 87 76 63 67 56 60 58 63 64 60 61
#> [586] 68 67 66 67 72 77 68 74 68 71 69 71 70 71 65
#> [601] 1744 1719 1764 1754 1751 1766 1730 1778 1780 1819 1829 1823 1860 1872 1918
#> [616] 1898 1957 1970 1980 1991 2001 1964 2045 2003 1997 253 246 247 217 233
#> [631] 207 315 230 204 198 305 327 284 313 308 235 233 333 229 223
#> [646] 227 200 211 237 255 269 354 306 289 245 304 229 273 240 333
#> [661] 337 372 272 233 253 234 267 305 227 275 308 296 222 240 239
#> [676] 297 358 256 241 332 360 256 322 302 317 310 298 295 330 281
#> [691] 283 241 238 340 268 289 330 250
#>
#> $data$y_smp
#> [1] 19 20 16 13 13 8 9 10 5 4 4 4 4 4 6
#> [16] 7 11 13 13 15 13 11 16 18 20 53 54 49 50 48
#> [31] 53 52 53 57 55 51 55 55 56 60 62 65 66 66 64
#> [46] 63 61 58 57 54 66 71 66 70 71 69 65 65 64 69
#> [61] 71 70 70 70 69 68 72 69 69 65 65 62 65 65 62
#> [76] 99 98 95 98 103 104 101 105 104 105 108 108 113 115 117
#> [91] 119 117 119 117 121 124 120 124 126 126 89 91 90 94 95
#> [106] 95 97 97 93 91 90 89 87 89 94 93 94 98 99 100
#> [121] 100 102 106 110 114 61 63 59 54 53 53 55 55 54 57
#> [136] 58 63 58 56 58 61 57 59 61 60 63 62 59 58 56
#> [151] 61 65 62 63 63 61 56 59 58 63 60 56 56 55 58
#> [166] 55 54 57 61 65 61 58 54 55 57 49 52 53 57 58
#> [181] 59 60 60 64 68 64 68 66 65 62 64 66 70 70 68
#> [196] 67 64 67 65 60 373 372 376 374 374 375 378 379 381 383
#> [211] 386 389 386 384 380 385 388 389 392 387 387 386 390 387 382
#> [226] 85 80 80 83 82 83 83 81 77 79 76 81 82 79 82
#> [241] 85 90 85 83 84 88 86 82 85 84 72 74 79 77 75
#> [256] 71 76 74 72 74 78 80 78 82 85 89 90 88 87 91
#> [271] 92 91 94 98 98 37 34 34 38 36 39 37 39 38 40
#> [286] 38 37 40 40 37 36 37 37 38 35 38 38 38 36 41
#> [301] 85 83 86 87 89 91 91 89 90 91 90 90 95 97 94
#> [316] 94 92 93 97 97 101 99 100 97 97 75 72 68 65 65
#> [331] 64 65 68 70 67 65 68 69 70 74 75 77 78 74 79
#> [346] 84 84 88 87 84 29 26 31 26 24 22 17 20 24 20
#> [361] 21 22 19 19 22 20 16 14 18 20 24 27 22 27 32
#> [376] 117 122 122 124 126 126 129 126 126 131 130 133 132 132 128
#> [391] 125 123 125 124 120 115 112 111 110 111 276 279 279 277 278
#> [406] 275 279 277 275 279 281 276 272 271 270 269 272 274 278 280
#> [421] 279 283 287 285 283 17 20 19 23 26 22 17 16 11 14
#> [436] 15 13 17 21 22 19 23 23 24 19 14 11 15 13 9
#> [451] 338 341 339 335 332 330 332 328 328 327 331 331 331 329 331
#> [466] 327 329 328 327 328 328 331 331 335 334 62 67 65 62 57
#> [481] 55 51 50 47 47 44 41 45 47 52 53 51 54 59 54
#> [496] 50 54 56 61 66 44 47 48 45 40 35 34 33 35 37
#> [511] 35 39 43 44 45 48 51 54 49 52 53 49 53 57 55
#> [526] 115 114 110 113 114 118 123 119 119 120 120 120 122 117 120
#> [541] 118 118 113 117 113 116 118 120 115 117 92 92 91 88 88
#> [556] 91 90 92 92 95 92 92 87 82 84 86 89 92 95 97
#> [571] 96 94 97 96 92 79 74 70 66 70 65 68 66 66 68
#> [586] 72 72 72 72 76 80 76 78 74 77 75 74 75 72 70
#> [601] 2154 2170 2148 2144 2142 2128 2128 2125 2115 2146 2142 2157 2159 2156 2183
#> [616] 2194 2212 2228 2243 2242 2246 2229 2257 2264 2254 285 280 290 250 255
#> [631] 235 345 265 230 215 340 345 310 415 400 311 289 436 290 296
#> [646] 292 251 259 303 329 344 432 363 355 307 367 267 333 279 386
#> [661] 396 433 318 276 297 276 328 374 282 343 378 368 268 291 294
#> [676] 357 435 303 281 394 418 289 363 342 367 339 337 330 362 311
#> [691] 313 269 273 385 292 325 374 301
#>
#> $data$n_weights
#> [1] 750
#>
#> $data$obs_to_weights_bounds
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
#> [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
#> [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
#> [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
#> [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
#> [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
#> [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
#> [253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270
#> [271] 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
#> [289] 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306
#> [307] 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324
#> [325] 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342
#> [343] 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360
#> [361] 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378
#> [379] 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396
#> [397] 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414
#> [415] 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
#> [433] 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450
#> [451] 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468
#> [469] 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486
#> [487] 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504
#> [505] 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522
#> [523] 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540
#> [541] 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558
#> [559] 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
#> [577] 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594
#> [595] 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612
#> [613] 613 614 615 616 617 618 619 620 621 622 623 624 625 626 631 636 641 646
#> [631] 651 656 661 666 671 676 681 686 691 692 693 694 695 696 697 698 699 700
#> [649] 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718
#> [667] 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
#> [685] 737 738 739 740 741 742 743 744 745 746 747 748 749 750
#>
#> $data$weights_school
#> [1] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
#> [26] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
#> [51] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [76] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
#> [101] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
#> [126] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
#> [151] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
#> [176] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
#> [201] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
#> [226] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
#> [251] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
#> [276] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
#> [301] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
#> [326] 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21
#> [351] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
#> [376] 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
#> [401] 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
#> [426] 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26
#> [451] 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
#> [476] 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27
#> [501] 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
#> [526] 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28
#> [551] 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24
#> [576] 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
#> [601] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [626] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [651] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [676] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [701] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [726] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#> $data$weights_cohort
#> [1] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [26] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [51] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [76] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [101] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [126] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [151] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [176] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [201] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [226] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [251] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [276] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [301] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [326] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [351] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [376] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [401] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [426] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [451] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [476] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [501] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [526] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [551] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [576] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [601] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> [626] 3 4 5 6 7 4 5 6 7 8 5 6 7 8 9 6 7 8 9 10 7 8 9 10 11
#> [651] 8 9 10 11 12 9 10 11 12 13 10 11 12 13 14 11 12 13 14 15 12 13 14 15 16
#> [676] 13 14 15 16 17 14 15 16 17 18 15 16 17 18 19 2 3 4 5 6 7 8 9 10 11
#> [701] 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5
#> [726] 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
#>
#> $data$weights_life_year
#> [1] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [26] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [51] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [76] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [101] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [126] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [151] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [176] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [201] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [226] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [251] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [276] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [301] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [326] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [351] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [376] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [401] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [426] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [451] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [476] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [501] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [526] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [551] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [576] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [601] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> [626] 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14
#> [651] 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14
#> [676] 18 17 16 15 14 18 17 16 15 14 18 17 16 15 14 2 2 2 2 2 2 2 2 2 2
#> [701] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3
#> [726] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>
#> $data$weights_dose
#> [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [741] 1 1 1 1 1 1 1 1 1 1
#>
#> $data$weights
#> [1] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [19] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [37] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [55] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [73] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [91] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [109] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [127] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [145] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [163] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [181] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [199] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [217] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [235] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [253] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [271] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [289] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [307] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [325] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [343] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [361] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [379] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [397] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [415] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [433] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [451] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [469] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [487] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [505] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [523] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [541] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [559] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [577] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [595] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [613] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.2 0.2 0.2 0.2 0.2
#> [631] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
#> [649] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
#> [667] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
#> [685] 0.2 0.2 0.2 0.2 0.2 0.2 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [703] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [721] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#> [739] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
#>
#> $data$n_cnty
#> [1] 3
#>
#> $data$cnty_bounds
#> [1] 1 11 18
#>
#> $data$predict_mode
#> [1] 0
#>
#>
#> $locations
#> Key: <layer, parent_id, loc_id>
#> loc_id parent_id layer loc_c_id loc_cp_id layer_bound
#> <char> <char> <int> <int> <int> <int>
#> 1: State <NA> 1 1 NA 1
#> 2: Scruggs State 2 2 1 1
#> 3: Simone State 2 3 1 1
#> 4: Watson State 2 4 1 1
#> 5: Blue Heron School Scruggs 3 5 2 1
#> 6: Bluebird Learning Center Scruggs 3 6 2 1
#> 7: Catbird Academy Scruggs 3 7 2 1
#> 8: Chickadee Elementary Scruggs 3 8 2 1
#> 9: Finch Elementary Scruggs 3 9 2 1
#> 10: Flycatcher Elementary Scruggs 3 10 2 1
#> 11: Nuthatch Academy Scruggs 3 11 2 1
#> 12: Sparrow School Scruggs 3 12 2 1
#> 13: Towhee Children's Academy Scruggs 3 13 2 1
#> 14: Warbler Elementary Scruggs 3 14 2 1
#> 15: Bunting School Simone 3 15 3 11
#> 16: Cardinal Academy Simone 3 16 3 11
#> 17: Egret Elementary Simone 3 17 3 11
#> 18: Grosbeak Learning Center Simone 3 18 3 11
#> 19: Junco Elementary Simone 3 19 3 11
#> 20: Oriole Youth Academy Simone 3 20 3 11
#> 21: Tanager Academy Simone 3 21 3 11
#> 22: Cormorant Elementary Watson 3 22 4 18
#> 23: Goldfinch Elementary Watson 3 23 4 18
#> 24: Kingfisher Academy Watson 3 24 4 18
#> 25: Kinglet Learning Center Watson 3 25 4 18
#> 26: Meadowlark School Watson 3 26 4 18
#> 27: Mockingbird Academy Watson 3 27 4 18
#> 28: Vireo School Watson 3 28 4 18
#> loc_id parent_id layer loc_c_id loc_cp_id layer_bound
#> <char> <char> <int> <int> <int> <int>
#>
#> attr(,"class")
#> [1] "imugap_fit"
# }