Obtain upper level model estimates
ec_estimates_MU(est, quantiles = c(0.05, 0.95))is an 'echoice2' draw object (list)
quantile for CI
tibble with MU (upper level) summaries
data(icecream)
#run MCMC sampler (use way more than 20 draws for actual use)
icecream_est <- icecream %>% dplyr::filter(id<20) %>% vd_est_vdm(R=20, cores=2)
#> Using 2 cores
#> MCMC in progress
#> MCMC complete
#> Total Time Elapsed: 0.02 minutes
#Upper-level summary
icecream_est %>% ec_estimates_MU
#> # A tibble: 21 × 12
#> attribute lvl par mean sd `CI-5%` `CI-95%` sig model error
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <lgl> <chr> <chr>
#> 1 NA NA int 0.00250 0.213 -0.133 0.138 FALSE VD-c… EV1
#> 2 Brand BlueBell Bran… 0.294 0.321 0.0896 0.498 TRUE VD-c… EV1
#> 3 Brand BlueBunny Bran… -0.0384 0.0531 -0.0722 -0.00460 TRUE VD-c… EV1
#> 4 Brand Breyers Bran… -0.248 0.0411 -0.275 -0.222 TRUE VD-c… EV1
#> 5 Brand Dryers Bran… -0.147 0.214 -0.283 -0.0108 TRUE VD-c… EV1
#> 6 Brand HaagenDa Bran… 0.0730 0.545 -0.274 0.420 FALSE VD-c… EV1
#> 7 Brand Store Bran… 0.120 0.162 0.0168 0.224 TRUE VD-c… EV1
#> 8 Flavor ChocChip Flav… -0.0479 0.0354 -0.0705 -0.0254 TRUE VD-c… EV1
#> 9 Flavor ChocDough Flav… 0.292 0.101 0.228 0.357 TRUE VD-c… EV1
#> 10 Flavor CookieCre… Flav… -0.178 0.132 -0.262 -0.0937 TRUE VD-c… EV1
#> # … with 11 more rows, and 2 more variables: reference_lvl <chr>,
#> # parameter <chr>