This package contains choice models with economic foundation. Its purpose is to simplify using choice models with economic foundation. Key tenets are: (1) Simple, flexible data handling that is compatible with R-tidyverse and general enough to support many different models (2) speed.
For more theoretical background and reasons to use choice models with economic foundation, please refer to the chapter ‘Economic foundations of conjoint analysis’ in the Handbook of the Economics of Marketing.
All key functions are written in c++ and use openMP for multi-threaded computing. C++ integration in R is facilitated by Rcpp.
echoice2 (largely) follows tidy principles and integrated nicely with
dplyr. It can be used to generate choice volume/share simulators,
though no front-end is built into the package yet.
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Looking for co-developers!
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Version 0.2.4:
- A lot of work because of Roxygen’s breaking change, from recommended use of docType to breaking it
- Nothing changed functionality-wise
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Version 0.2.3:
- The vignette “Importing list-of-lists choice data and discrete choice modeling with echoice2” demonstrates how to import list-of-lists style choice data, convert it for use with echoice2 and fit choice models. Related convenience functions have been added to the package.
- CRAN release to follow soon
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Version 0.2.2:
- Added a vignette on volumetric choice modeling with and without conjunctive screening
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Version 0.2.1:
- Package compiles in absense of OpenMP Support
- MacOS CRAN binaries are compiled without OpenMP - for full speed compile yourself or use binaries supplied here
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Version 0.2.0:
- Initial CRAN release
- No new functionality, but cleaner code
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Version 0.1.6:
- Stability/Performance improvements for demand simulators
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Version 0.1.5:
- Initial release
- faster and more efficient screening model estimation
- improved demand predictions: posterior demand draws are now stored in a single column - this is a major improvement for dealing with demand predictions!
- some bug-fixes and documentation improvements
#install from CRAN
# install.packages("echoice2")
#install from github
# install.packages("remotes")
# remotes::install_github("ninohardt/echoice2", build_vignettes = TRUE)library(echoice2)- If you use Linux it should just work.
- If you are using Windows, install Rtools first.
- If you are using OSX, you may have to install CLI, XQuartz and potentially other things that Apple removed from OSX. For multicore support, you also need to find a compiler that does support OpenMP. Just google it.
The following models are implemented (including estimation and prediction):
- Discrete Choice (HMNL)
- Without Screening
- With conjunctive Screening
- Volumetric demand (EV1, Normal errors)
- Without Screening
- With conjunctive Screening
- With set-size variation
- upper-level covariates, effects-coding, discrete and continuous attributes, discrete choice example vignette, …
Functions that relate to discrete demand start in dd_, while functions
for volumetric demand start in vd_. Universal functions (discrete and
volumetric choice) start in ec_. Estimation functions continue in
est, demand simulators in dem.
The package comes with a small example dataset icecream from a
volumetric conjoint study. It contains 300 respondents.
data(icecream)
icecream %>% head
#> # A tibble: 6 × 8
#> id task alt x p Brand Flavor Size
#> <int> <int> <int> <dbl> <dbl> <fct> <fct> <ord>
#> 1 1 1 1 8 0.998 Store Neapolitan 16
#> 2 1 1 2 0 0.748 Store VanillaBean 16
#> 3 1 1 3 0 1.25 BenNJerry Oreo 16
#> 4 1 1 4 0 0.748 BenNJerry Neapolitan 16
#> 5 1 1 5 0 2.49 HaagenDa RockyRoad 4
#> 6 1 1 6 0 1.25 HaagenDa Oreo 16Choice data data.frames or tibbles need to contain the following columns:
id(integer; respondent identifier)task(integer; task number)alt(integer; alternative number within task)x(double; quantity purchased)p(double; price)- attributes defining the choice alternatives (factor, and soon continuous as well)
While this requires a little extra space for discrete choice data, it simplifies the workflow and makes the package versatile. It can be applied to data from choice experiments and purchase histories. It allows variance in the number of choice tasks per subject, and variance in the number of choice alternatives per task.
Estimating a simple volumetric demand model is easy. Use the
vd_est_vdm function, and use at least 100,000 draws:
est_icecream <- icecream %>% vd_est_vdm(R=10000)
#> Using 16 cores
#> MCMC in progress
#> MCMC complete
#> Total Time Elapsed: 0.17 minutesUpper-level estimates can be summarized using ec_estimates_MU:
est_icecream %>% 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 -3.22 0.547 -3.56 -2.52 TRUE VD-c… EV1
#> 2 Brand BlueBell Brand… -0.713 0.169 -0.944 -0.471 TRUE VD-c… EV1
#> 3 Brand BlueBunny Brand… -0.731 0.169 -0.945 -0.394 TRUE VD-c… EV1
#> 4 Brand Breyers Brand… -0.117 0.0976 -0.295 0.0331 FALSE VD-c… EV1
#> 5 Brand Dryers Brand… -0.554 0.122 -0.705 -0.358 TRUE VD-c… EV1
#> 6 Brand HaagenDa Brand… -0.358 0.0937 -0.510 -0.211 TRUE VD-c… EV1
#> 7 Brand Store Brand… -0.526 0.126 -0.707 -0.348 TRUE VD-c… EV1
#> 8 Flavor ChocChip Flavo… -0.393 0.113 -0.566 -0.213 TRUE VD-c… EV1
#> 9 Flavor ChocDough Flavo… -0.435 0.128 -0.618 -0.192 TRUE VD-c… EV1
#> 10 Flavor CookieCream Flavo… -0.443 0.111 -0.611 -0.256 TRUE VD-c… EV1
#> # ℹ 11 more rows
#> # ℹ 2 more variables: reference_lvl <chr>, parameter <chr>Corresponding demand predictions can be obtained using the vd_dem_vdm
function. Here, we generate in-sample predictions:
dempres_icecream <-
icecream %>%
vd_dem_vdm(est = est_icecream)
#> Using 16 coresThe resulting output makes it easy to work with demand predictions
without obtaining posterior means too early. Demand prediction draws are
stored in a single column .demdraws.
dempres_icecream
#> # A tibble: 39,600 × 9
#> id task alt x p Brand Flavor Size .demdraws
#> * <int> <int> <int> <dbl> <dbl> <fct> <fct> <ord> <list>
#> 1 1 1 1 8 0.998 Store Neapolitan 16 <dbl [1,000]>
#> 2 1 1 2 0 0.748 Store VanillaBean 16 <dbl [1,000]>
#> 3 1 1 3 0 1.25 BenNJerry Oreo 16 <dbl [1,000]>
#> 4 1 1 4 0 0.748 BenNJerry Neapolitan 16 <dbl [1,000]>
#> 5 1 1 5 0 2.49 HaagenDa RockyRoad 4 <dbl [1,000]>
#> 6 1 1 6 0 1.25 HaagenDa Oreo 16 <dbl [1,000]>
#> 7 1 1 7 0 1.12 BlueBunny Oreo 16 <dbl [1,000]>
#> 8 1 1 8 0 1.99 BlueBunny Neapolitan 4 <dbl [1,000]>
#> 9 1 1 9 0 0.622 Breyers RockyRoad 16 <dbl [1,000]>
#> 10 1 1 10 0 3.49 Breyers Vanilla 4 <dbl [1,000]>
#> # ℹ 39,590 more rowsWe can aggregate (e.g., by subject id) using ec_dem_aggregate:
dempres_icecream %>%
ec_dem_aggregate('id')
#> # A tibble: 300 × 2
#> id .demdraws
#> <int> <list>
#> 1 1 <dbl [1,000]>
#> 2 2 <dbl [1,000]>
#> 3 3 <dbl [1,000]>
#> 4 4 <dbl [1,000]>
#> 5 5 <dbl [1,000]>
#> 6 6 <dbl [1,000]>
#> 7 7 <dbl [1,000]>
#> 8 8 <dbl [1,000]>
#> 9 9 <dbl [1,000]>
#> 10 10 <dbl [1,000]>
#> # ℹ 290 more rowsOnce we have the desired aggregation level, we can obtain summaries
(e.g., posterior means) using ec_dem_summarise
dempres_icecream %>%
ec_dem_aggregate('id') %>%
ec_dem_summarise()
#> # A tibble: 300 × 6
#> id .demdraws `E(demand)` `S(demand)` `CI-5%` `CI-95%`
#> <int> <list> <dbl> <dbl> <dbl> <dbl>
#> 1 1 <dbl [1,000]> 39.5 12.6 20.7 62.5
#> 2 2 <dbl [1,000]> 99.1 27.5 56.7 146.
#> 3 3 <dbl [1,000]> 31.6 6.05 21.7 41.5
#> 4 4 <dbl [1,000]> 87.9 28.9 48.0 138.
#> 5 5 <dbl [1,000]> 32.2 17.9 10.7 68.2
#> 6 6 <dbl [1,000]> 16.1 8.78 4.96 33.6
#> 7 7 <dbl [1,000]> 72.1 22.4 47.7 110.
#> 8 8 <dbl [1,000]> 49.7 20.5 21.5 88.9
#> 9 9 <dbl [1,000]> 13.7 4.66 6.48 22.3
#> 10 10 <dbl [1,000]> 38.0 11.5 19.3 56.7
#> # ℹ 290 more rowsBoth ec_dem_aggregate and ec_dem_summarise simply apply common
dplyr and purrr functions.