library(diseasenowcasting)
# Create a fake disease process
set.seed(265824)
sims <- simulate_process_for_testing()
# Run a nowcast with very few iterations
predictions <- nowcast(sims, "onset_date", "report_date", cores = 4)
#> ℹ Computing a nowcast for 2024-09-11 per "days"
#> ℹ Assuming data is count-data where counts are in column `n`. To change this set `data_type = "linelist"`
#Get the predicted values
preds <- predictions |>
posterior::as_draws() |>
posterior::subset_draws("N_predict") |>
posterior::summarise_draws()
preds
#> # A tibble: 10 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 N_predict[1,1] 7485 7485 0 0 7485 7485 NA NA NA
#> 2 N_predict[2,1] 7178 7178 0 0 7178 7178 NA NA NA
#> 3 N_predict[3,1] 16581 16581 0 0 16581 16581 NA NA NA
#> 4 N_predict[4,1] 28006. 28005 3.05 2.97 28003 28012 1.00 4082. 3605.
#> 5 N_predict[5,1] 16233. 16232 4.49 4.45 16228 16242 1.00 4062. 4093.
#> 6 N_predict[6,1] 14445. 14444 5.43 4.45 14438 14456 0.999 3876. 3760.
#> 7 N_predict[7,1] 6076. 6075 6.20 5.93 6067 6087 1.00 3798. 3599.
#> 8 N_predict[8,1] 2219. 2217 7.06 5.93 2209 2232 1.00 3821. 4016.
#> 9 N_predict[9,1] 3486. 3485 7.76 7.41 3476 3500 1.00 4150. 4082.
#> 10 N_predict[10,… 56870. 56869 8.48 7.41 56858 56886 1.00 4161. 3921.