Adds summaries of posterior draws of demand to tibble. (using the new demand draw format)
ec_dem_summarise(de,quantiles)
ec_dem_summarize(de, quantiles = c(0.05, 0.95))demand draws
Quantiles for Credibility Intervals (default: 90% interval)
Summary of demand predictions
# \donttest{
data(icecream)
#run MCMC sampler (use way more than 10 draws for actual use)
icecream_est <- icecream %>% dplyr::filter(id<10) %>% vd_est_vdm(R=10, keep=1)
#> Using 16 cores
#> MCMC in progress
#> MCMC complete
#> Total Time Elapsed: 0.00 minutes
#Generate demand predictions
icecream_predicted_demand=
icecream %>% dplyr::filter(id<10) %>%
vd_dem_vdm(icecream_est)
#> Using 16 cores
#aggregate
brand_lvl_pred_demand <-
icecream_predicted_demand %>% ec_dem_aggregate("Brand")
#summarise
brand_lvl_pred_demand %>% ec_dem_summarise()
#> # A tibble: 7 × 6
#> Brand .demdraws `E(demand)` `S(demand)` `CI-5%` `CI-95%`
#> <fct> <list> <dbl> <dbl> <dbl> <dbl>
#> 1 BenNJerry <dbl [10]> 119. 22.3 91.3 152.
#> 2 BlueBell <dbl [10]> 133. 27.9 86.4 162.
#> 3 BlueBunny <dbl [10]> 95.5 30.1 52.4 134.
#> 4 Breyers <dbl [10]> 112. 25.2 77.5 147.
#> 5 Dryers <dbl [10]> 99.5 27.8 67.4 137.
#> 6 HaagenDa <dbl [10]> 128. 25.6 92.1 168.
#> 7 Store <dbl [10]> 119. 15.5 103. 142.
# }