Summarize attribute-based screening parameters from an attribute-based screening model in 'echoice2'
ec_estimates_screen(est, quantiles = c(0.05, 0.95))is an 'echoice2' draw object (list) from a model with attribute-based screening
quantile for CI
tibble with screening summaries
#run MCMC sampler (use way more than 20 draws for actual use)
data(icecream)
est_scr_icecream <- vd_est_vdm_screen(icecream%>%dplyr::filter(id<30), R=20, cores=2)
#> Using 2 cores
#> MCMC in progress
#> MCMC complete
#> Total Time Elapsed: 0.00 minutes
#summarise draws of screening probabilities
ec_estimates_screen(est_scr_icecream)
#> # A tibble: 20 × 8
#> attribute lvl par mean sd `CI-5%` `CI-95%` limit
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Brand BenNJerry Brand:BenNJerry 0.5 0 0.5 0.5 NA
#> 2 Brand BlueBell Brand:BlueBell 0.5 0 0.5 0.5 NA
#> 3 Brand BlueBunny Brand:BlueBunny 0.5 0 0.5 0.5 NA
#> 4 Brand Breyers Brand:Breyers 0.5 0 0.5 0.5 NA
#> 5 Brand Dryers Brand:Dryers 0.5 0 0.5 0.5 NA
#> 6 Brand HaagenDa Brand:HaagenDa 0.5 0 0.5 0.5 NA
#> 7 Brand Store Brand:Store 0.5 0 0.5 0.5 NA
#> 8 Flavor ChocChip Flavor:ChocChip 0.5 0 0.5 0.5 NA
#> 9 Flavor ChocDough Flavor:ChocDough 0.5 0 0.5 0.5 NA
#> 10 Flavor Chocolate Flavor:Chocolate 0.5 0 0.5 0.5 NA
#> 11 Flavor CookieCream Flavor:CookieCream 0.5 0 0.5 0.5 NA
#> 12 Flavor Neapolitan Flavor:Neapolitan 0.5 0 0.5 0.5 NA
#> 13 Flavor Oreo Flavor:Oreo 0.5 0 0.5 0.5 NA
#> 14 Flavor RockyRoad Flavor:RockyRoad 0.5 0 0.5 0.5 NA
#> 15 Flavor Vanilla Flavor:Vanilla 0.5 0 0.5 0.5 NA
#> 16 Flavor VanillaBean Flavor:VanillaBean 0.5 0 0.5 0.5 NA
#> 17 Flavor VanillaFudge Flavor:VanillaFudge 0.5 0 0.5 0.5 NA
#> 18 Size 16 Size:16 0.5 0 0.5 0.5 NA
#> 19 Size 4 Size:4 0.5 0 0.5 0.5 NA
#> 20 Size 8 Size:8 0.5 0 0.5 0.5 NA
#Note: There is no variance in this illustrative example - more draws are needed