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Merged
merged 3 commits into from
Jul 30, 2023
Merged

allow snapshots shorter than trunc_max #438

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2589528
allow snapshots shorter than trunc_max
sbfnk Jul 25, 2023
50d835d
update NEWS
sbfnk Jul 25, 2023
ba060db
rephrase bug -> feature
sbfnk Jul 28, 2023
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allow snapshots shorter than trunc_max
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@sbfnk
sbfnk committed Jul 30, 2023
commit 2589528354ace801b7dccae3b6ff83f9257e8052
2 changes: 1 addition & 1 deletion R/estimate_truncation.R
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Expand Up @@ -151,7 +151,7 @@ estimate_truncation <- function(obs, max_truncation, trunc_max = 10,
confirm := NULL
])
obs <- purrr::reduce(obs, merge, all = TRUE)
obs_start <- nrow(obs) - trunc_max - sum(is.na(obs$`1`)) + 1
obs_start <- max(nrow(obs) - trunc_max - sum(is.na(obs$`1`)) + 1, 1)
obs_dist <- purrr::map_dbl(2:(ncol(obs)), ~ sum(is.na(obs[[.]])))
obs_data <- obs[, -1][, purrr::map(.SD, ~ ifelse(is.na(.), 0, .))]
obs_data <- obs_data[obs_start:.N]
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37 changes: 25 additions & 12 deletions inst/stan/estimate_truncation.stan
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Expand Up @@ -11,14 +11,23 @@ data {
int trunc_max;
int trunc_dist;
}
transformed data{
int<lower = 1> end_t[obs_sets];
int<lower = 1> start_t[obs_sets];
for (i in 1:obs_sets) {
end_t[i] = t - obs_dist[i];
start_t[i] = max(1, end_t[i] - trunc_max + 1);
}
}
parameters {
real logmean;
real<lower=0> logsd;
real<lower=0> phi;
real<lower=0> sigma;
}
transformed parameters{
matrix[trunc_max, obs_sets - 1] trunc_obs;
matrix[trunc_max, obs_sets - 1] trunc_obs =
rep_matrix(0, trunc_max, obs_sets - 1);
real sqrt_phi = 1 / sqrt(phi);
vector[trunc_max] rev_cmf = reverse_mf(cumulative_sum(
discretised_pmf(logmean, logsd, trunc_max, trunc_dist)
Expand All @@ -31,9 +40,8 @@ transformed parameters{
// apply truncation to latest dataset to map back to previous data sets and
// add noise term
for (i in 1:(obs_sets - 1)) {
int end_t = t - obs_dist[i];
int start_t = end_t - trunc_max + 1;
trunc_obs[, i] = truncate(last_obs[start_t:end_t], rev_cmf, 0) + sigma;
trunc_obs[1:(end_t[i] - start_t[i] + 1), i] =
truncate(last_obs[start_t[i]:end_t[i]], rev_cmf, 0) + sigma;
}
}
}
Expand All @@ -45,23 +53,28 @@ model {
sigma ~ normal(0, 1) T[0,];
// log density of truncated latest data vs that observed
for (i in 1:(obs_sets - 1)) {
int start_t = t - obs_dist[i] - trunc_max;
for (j in 1:trunc_max) {
obs[start_t + j, i] ~ neg_binomial_2(trunc_obs[j, i], sqrt_phi);
for (j in 1:(end_t[i] - start_t[i] + 1)) {
obs[start_t[i] + j - 1, i] ~ neg_binomial_2(trunc_obs[j, i], sqrt_phi);
}
}
}
generated quantities {
matrix[trunc_max, obs_sets] recon_obs;
matrix[trunc_max, obs_sets] recon_obs = rep_matrix(0, trunc_max, obs_sets);
matrix[trunc_max, obs_sets - 1] gen_obs;
// reconstruct all truncated datasets using posterior of the truncation distribution
for (i in 1:obs_sets) {
int end_t = t - obs_dist[i];
int start_t = end_t - trunc_max + 1;
recon_obs[, i] = truncate(to_vector(obs[start_t:end_t, i]), rev_cmf, 1);
recon_obs[1:(end_t[i] - start_t[i] + 1), i] = truncate(
to_vector(obs[start_t[i]:end_t[i], i]), rev_cmf, 1
);
}
// generate observations for comparing
for (i in 1:(obs_sets - 1)) {
gen_obs[, i] = to_vector(neg_binomial_2_rng(trunc_obs[, i], sqrt_phi));
for (j in 1:trunc_max) {
if (trunc_obs[j, i] == 0) {
gen_obs[j, i] = 0;
} else {
gen_obs[j, i] = neg_binomial_2_rng(trunc_obs[j, i], sqrt_phi);
}
}
}
}
8 changes: 4 additions & 4 deletions inst/stan/functions/observation_model.stan
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Expand Up @@ -24,13 +24,13 @@ vector truncate(vector reports, vector trunc_rev_cmf, int reconstruct) {
int t = num_elements(reports);
vector[t] trunc_reports = reports;
// Calculate cmf of truncation delay
int trunc_max = num_elements(trunc_rev_cmf);
int trunc_max = min(t, num_elements(trunc_rev_cmf));
int first_t = t - trunc_max + 1;
// Apply cdf of truncation delay to truncation max last entries in reports
if (reconstruct) {
trunc_reports[first_t:t] = trunc_reports[first_t:t] ./ trunc_rev_cmf;
}else{
trunc_reports[first_t:t] = trunc_reports[first_t:t] .* trunc_rev_cmf;
trunc_reports[first_t:t] ./= trunc_rev_cmf[1:trunc_max];
} else {
trunc_reports[first_t:t] .*= trunc_rev_cmf[1:trunc_max];
}
return(trunc_reports);
}
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