diff --git a/EpiAware/docs/src/examples/getting_started.jl b/EpiAware/docs/src/examples/getting_started.jl index 76e19eae4..ee200011d 100644 --- a/EpiAware/docs/src/examples/getting_started.jl +++ b/EpiAware/docs/src/examples/getting_started.jl @@ -1,5 +1,5 @@ ### A Pluto.jl notebook ### -# v0.19.39 +# v0.19.40 using Markdown using InteractiveUtils diff --git a/EpiAware/src/EpiObsModels/delayobservations.jl b/EpiAware/src/EpiObsModels/delayobservations.jl index b7ffbb157..e613bf19f 100644 --- a/EpiAware/src/EpiObsModels/delayobservations.jl +++ b/EpiAware/src/EpiObsModels/delayobservations.jl @@ -1,17 +1,19 @@ struct DelayObservations{T <: AbstractFloat, S <: Sampleable} <: AbstractObservationModel delay_kernel::SparseMatrixCSC{T, Integer} neg_bin_cluster_factor_prior::S + pos_shift::T function DelayObservations(delay_int, time_horizon, - neg_bin_cluster_factor_prior) + neg_bin_cluster_factor_prior; + pos_shift = 1e-6) @assert all(delay_int .>= 0) "Delay interval must be non-negative" @assert sum(delay_int)≈1 "Delay interval must sum to 1" K = generate_observation_kernel(delay_int, time_horizon) new{eltype(K), typeof(neg_bin_cluster_factor_prior)}(K, - neg_bin_cluster_factor_prior) + neg_bin_cluster_factor_prior, pos_shift) end function DelayObservations(; @@ -19,9 +21,11 @@ struct DelayObservations{T <: AbstractFloat, S <: Sampleable} <: AbstractObserva time_horizon::Integer, neg_bin_cluster_factor_prior::Sampleable, D_delay, - Δd = 1.0) + Δd = 1.0, + pos_shift = 1e-6) delay_int = create_discrete_pmf(delay_distribution; Δd = Δd, D = D_delay) - return DelayObservations(delay_int, time_horizon, neg_bin_cluster_factor_prior) + return DelayObservations( + delay_int, time_horizon, neg_bin_cluster_factor_prior; pos_shift) end end @@ -32,14 +36,13 @@ end @model function EpiAwareBase.generate_observations(observation_model::DelayObservations, y_t, - I_t; - pos_shift) + I_t) #Parameters neg_bin_cluster_factor ~ observation_model.neg_bin_cluster_factor_prior #Predictive distribution - expected_obs = observation_model.delay_kernel * I_t .+ pos_shift + expected_obs = observation_model.delay_kernel * I_t .+ observation_model.pos_shift if ismissing(y_t) y_t = Vector{Int}(undef, length(expected_obs)) diff --git a/EpiAware/src/make_epi_aware.jl b/EpiAware/src/make_epi_aware.jl index a4ea306a5..2e1959405 100644 --- a/EpiAware/src/make_epi_aware.jl +++ b/EpiAware/src/make_epi_aware.jl @@ -2,8 +2,8 @@ time_steps; epi_model::AbstractEpiModel, latent_model::AbstractLatentModel, - observation_model::AbstractObservationModel, - pos_shift = 1e-6) + observation_model::AbstractObservationModel +) #Latent process @submodel Z_t, latent_model_aux = generate_latent( latent_model, @@ -15,8 +15,7 @@ #Predictive distribution of ascerted cases @submodel generated_y_t, generated_y_t_aux = generate_observations(observation_model, y_t, - I_t; - pos_shift = pos_shift) + I_t) #Generate quantities return (; diff --git a/EpiAware/test/test_models.jl b/EpiAware/test/test_models.jl index 6e60b91ab..fabbc23a4 100644 --- a/EpiAware/test/test_models.jl +++ b/EpiAware/test/test_models.jl @@ -23,12 +23,13 @@ time_horizon = time_horizon, neg_bin_cluster_factor_prior = Gamma(5, 0.05 / 5), D_delay = D_delay, - Δd = Δd) + Δd = Δd; + pos_shift = pos_shift) # Create full epi model and sample from it test_mdl = make_epi_aware(y_t, time_horizon; epi_model = epi_model, latent_model = rwp, - observation_model = obs_model, pos_shift) + observation_model = obs_model) gen = generated_quantities(test_mdl, rand(test_mdl)) #Check model sampled @@ -57,15 +58,15 @@ end time_horizon = 5 obs_model = DelayObservations([1.0], time_horizon, - truncated(Gamma(5, 0.05 / 5), 1e-3, 1.0)) + truncated(Gamma(5, 0.05 / 5), 1e-3, 1.0); + pos_shift) # Create full epi model and sample from it test_mdl = make_epi_aware(y_t, time_horizon; epi_model = epi_model, latent_model = rwp, - observation_model = obs_model, - pos_shift) + observation_model = obs_model) chn = sample(test_mdl, Prior(), 1000) gens = generated_quantities(test_mdl, chn) @@ -96,15 +97,16 @@ end time_horizon = 5 obs_model = DelayObservations([1.0], time_horizon, - truncated(Gamma(5, 0.05 / 5), 1e-3, 1.0)) + truncated(Gamma(5, 0.05 / 5), 1e-3, 1.0); + pos_shift) # Create full epi model and sample from it test_mdl = make_epi_aware(y_t, time_horizon; epi_model = epi_model, latent_model = rwp, - observation_model = obs_model, - pos_shift) + observation_model = obs_model + ) chn = sample(test_mdl, Prior(), 1000) gens = generated_quantities(test_mdl, chn) diff --git a/EpiAware/test/test_observation-models.jl b/EpiAware/test/test_observation-models.jl index 50d63e72d..b067bf135 100644 --- a/EpiAware/test/test_observation-models.jl +++ b/EpiAware/test/test_observation-models.jl @@ -8,7 +8,8 @@ # Delay kernel is just event observed on same day delay_obs = DelayObservations([1.0], length(I_t), - obs_prior[:neg_bin_cluster_factor_prior]) + obs_prior[:neg_bin_cluster_factor_prior]; + pos_shift = 1e-6) # Set up priors neg_bin_cf = 0.05 @@ -16,8 +17,7 @@ # Call the function mdl = generate_observations(delay_obs, missing, - I_t; - pos_shift = 1e-6) + I_t) fix_mdl = fix(mdl, (neg_bin_cluster_factor = neg_bin_cf,)) n_samples = 1000 @@ -52,10 +52,10 @@ end # Define a common setup for your model that can be reused across different y_t scenarios obs_prior = default_delay_obs_priors() delay_obs = DelayObservations( - [1.0], length(I_t), obs_prior[:neg_bin_cluster_factor_prior]) + [1.0], length(I_t), obs_prior[:neg_bin_cluster_factor_prior]; + pos_shift = 1e-6) neg_bin_cf = 0.05 # Set up priors # Expected point estimate calculation setup - pos_shift = 1e-6 # Test each y_t scenario for (scenario_name, y_t_scenario) in [("fully observed", y_t_fully_observed), @@ -63,7 +63,7 @@ end ("fully unobserved", y_t_fully_unobserved)] @testset "$scenario_name y_t" begin mdl = generate_observations( - delay_obs, y_t_scenario, I_t; pos_shift = pos_shift) + delay_obs, y_t_scenario, I_t) sampled_obs = sample(mdl, Prior(), 1000) |> chn -> generated_quantities(mdl, chn) .|> (gen -> gen[1]) |>