Demand Prediction (Volumetric Demand Model)

Source: R/echoice2.R

Generating demand predictions for volumetric demand model. Reminder: there is no closed-form solution for demand, thus we need to integrate not only over the posterior distribution of parameters and the error distribution. The function outputs a tibble containing id, task, alt, p, attributes, draws from the posterior of demand. Error realizations can be pre-supplied to the epsilon_not. This helps create smooth demand curves or conduct optimization.

vd_dem_vdm(
  vd,
  est,
  epsilon_not = NULL,
  error_dist = NULL,
  tidy = TRUE,
  cores = NULL
)

Arguments

vd

data

est

ec-model draws

epsilon_not

(optional) error realizations

error_dist

(optional) A string defining the error term distribution (default: 'EV1')

tidy

(optional) apply 'echoice2' tidier (default: TRUE)

cores

(optional) cores (default: auto-detect)

Value

Draws of expected demand

See also

prep_newprediction() to match vd's factor levels, ec_gen_err_ev1() for pre-generating error realizations and vd_est_vdm() for estimating the corresponding model

Examples

data(icecream)
#run MCMC sampler (use way more than 10 draws for actual use)
icecream_est <- icecream %>% dplyr::filter(id<20) %>% vd_est_vdm(R=10, keep=1, cores=2)
#> Using 2 cores
#>  MCMC in progress 
#> MCMC complete
#>  Total Time Elapsed: 0.00 minutes
#Generate demand predictions
icecream_predicted_demand=
 icecream %>% dplyr::filter(id<20) %>%   
   vd_dem_vdm(icecream_est, cores=2)
#> Using 2 cores
#column .demdraws contains draws from posterior of predicted demand