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Showing content from https://github.com/ninohardt/echoice2 below:

ninohardt/echoice2: choice models with economic foundation

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.

• Looking for co-developers!

• 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

• 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

• Version 0.2.2:

  • Added a vignette on volumetric choice modeling with and without conjunctive screening

• 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

• Version 0.2.0:

  • Initial CRAN release
  • No new functionality, but cleaner code

• Version 0.1.6:

  • Stability/Performance improvements for demand simulators

• 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)

• 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        16

Choice 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 minutes

Upper-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 cores

The 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 rows

We 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 rows

Once 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 rows

Both ec_dem_aggregate and ec_dem_summarise simply apply common dplyr and purrr functions.

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