Summarize posterior draws of demand (volumetric models only)

Source: R/echoice2.R

Adds summaries of posterior draws of demand to tibble. (using the new demand draw format)

vd_dem_summarise(de, quantiles = c(0.05, 0.95))

vd_dem_summarize(de, quantiles = c(0.05, 0.95))

Arguments

de

demand draws

quantiles

Quantiles for Credibility Intervals (default: 90% interval)

Value

Summary of demand predictions

Examples

# \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 %>% vd_dem_summarise()
#> # A tibble: 7 × 8
#>   Brand     .demdraws  `E(demand)` `S(demand)` `CI-5%` `CI-95%` E(inte…¹ S(int…²
#>   <fct>     <list>           <dbl>       <dbl>   <dbl>    <dbl>    <dbl>   <dbl>
#> 1 BenNJerry <dbl [10]>        137.        33.6   112.      193.        1       0
#> 2 BlueBell  <dbl [10]>        144.        30.3   110.      194.        1       0
#> 3 BlueBunny <dbl [10]>        118.        26.3    90.0     162.        1       0
#> 4 Breyers   <dbl [10]>        127.        39.8    71.7     189.        1       0
#> 5 Dryers    <dbl [10]>        116.        42.2    62.7     185.        1       0
#> 6 HaagenDa  <dbl [10]>        117.        27.1    82.8     156.        1       0
#> 7 Store     <dbl [10]>        147.        63.3    86.9     247.        1       0
#> # … with abbreviated variable names ¹​`E(interior)`, ²​`S(interior)`
# }