# Model file examples

#### 2021-07-30

Apollo is an R package designed for estimation and analysis of choice models (Train, 2008). This package allows estimating Multinomial logit (MNL), Nested logit (NL), cross-nested logit (CNL), exploded logit (EL), ordered logit (OL), Integrated Choice and Latent Variable (ICLV, Ben-Akiva et al. 2002), Multiple Discrete-Continuous Extreme Value (MDCEV, Bhat 2008), nested MDCEV (MDCNEV, Pinjari and Bhat 2010), and Decision Field Theory (DFT, Hancock and Hess 2018) models. All models support both continuous and discrete mixing (e.g. continuous random parameters, latent classes or finite mixtures), both between and within individuals (i.e at the individual and observation level). Different models can be easily combined for joint estimation. The package allows for classical estimation (i.e. maximum likelihood) as well as Bayesian estimation (i.e. hierarchical Bayes, through package RSGHB).

All functionalities are described in the manual, available at www.ApolloChoiceModelling.com. Examples can also be found on the website.

## MNL model file example

In this section, we present code to estimate an MNL model using the synthetic data included in the package. In the postprocessing, we predict the impact on mode share of a 10% increase in the cost of the train fare. The utility functions of alternatives are defined as follows.

$U_{nsi} = asc_i + \beta_{tt}tt_{nsi} + \beta_{c}*cost_{nsi} + \varepsilon_{nsi}$

Where n indexes individuals, s choice scenarios, and i alternatives. $$asc_i$$ is the alternative specific constant, $$tt_{nsi}$$ is the travel time and $$cost_{nsi}$$ is the cost. $$\varepsilon_{nsi}$$ is an independent identically distributed standard Gumbel error term. $$asc_i$$, $$\beta_{tt}$$ and $$\beta_{c}$$ are parameters to be estimated.

The likelihood function of this model for individual $$n$$ is as follows.

$L_{n}=\prod_{s}P_{nsi}$

Where $P_{nsi}=\frac{e^{V_{nsi}}}{\sum_{j}e^{V_{nsj}}}$ And $$V_{nsi}=U_{nsi}-\varepsilon_{nsi}$$, i.e. the deterministic part of the utility.

The likelihood function of the MNL model is pre-coded in Apollo, so we do not need to type it ourselves. However, if the user prefers to write the likelihood themselves, they can also do it. The pre-coded MNL likelihood function (apollo_mnl) requires a series of inputs defined inside the mnl_settings object.

# ####################################################### #
#### 1. Definition of core settings
# ####################################################### #

### Clear memory
rm(list = ls())

library(apollo)
#> Apollo 0.2.5
#> www.ApolloChoiceModelling.com
#> See url for a detailed manual, examples and a help forum.

### Initialise code
apollo_initialise()

### Set core controls
apollo_control = list(
modelName  ="MNL",
modelDescr ="Simple MNL model on mode choice SP data",
indivID    ="ID"
)

# ####################################################### #
# ####################################################### #

data("apollo_modeChoiceData", package="apollo")
database = apollo_modeChoiceData
rm(apollo_modeChoiceData)

### Use only SP data
database = subset(database,database$SP==1) # ####################################################### # #### 3. Parameter definition #### # ####################################################### # ### Vector of parameters, including any that are kept fixed ### during estimation apollo_beta=c(asc_car = 0, asc_bus = 0, asc_air = 0, asc_rail = 0, b_tt_car = 0, b_tt_bus = 0, b_tt_air = 0, b_tt_rail= 0, b_c = 0) ### Vector with names (in quotes) of parameters to be ### kept fixed at their starting value in apollo_beta. ### Use apollo_beta_fixed = c() for no fixed parameters. apollo_fixed = c("asc_car") # ####################################################### # #### 4. Input validation #### # ####################################################### # apollo_inputs = apollo_validateInputs() #> Several observations per individual detected based on the value of ID. #> Setting panelData in apollo_control set to TRUE. #> All checks on apollo_control completed. #> All checks on database completed. # ####################################################### # #### 5. Likelihood definition #### # ####################################################### # apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){ ### Attach inputs and detach after function exit apollo_attach(apollo_beta, apollo_inputs) on.exit(apollo_detach(apollo_beta, apollo_inputs)) ### Create list of probabilities P P = list() ### List of utilities: these must use the same names as ### in mnl_settings, order is irrelevant. V = list() V[['car']] = asc_car + b_tt_car *time_car + b_c*cost_car V[['bus']] = asc_bus + b_tt_bus *time_bus + b_c*cost_bus V[['air']] = asc_air + b_tt_air *time_air + b_c*cost_air V[['rail']]= asc_rail+ b_tt_rail*time_rail+ b_c*cost_rail ### Define settings for MNL model component mnl_settings = list( alternatives = c(car=1, bus=2, air=3, rail=4), avail = list(car=av_car, bus=av_bus, air=av_air, rail=av_rail), choiceVar = choice, V = V ) ### Compute probabilities using MNL model P[['model']] = apollo_mnl(mnl_settings, functionality) ### Take product across observation for same individual P = apollo_panelProd(P, apollo_inputs, functionality) ### Prepare and return outputs of function P = apollo_prepareProb(P, apollo_inputs, functionality) return(P) } # ####################################################### # #### 6. Model estimation and reporting #### # ####################################################### # model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs, list(writeIter=FALSE)) #> #> Testing likelihood function... #> #> Overview of choices for MNL model component : #> car bus air rail #> Times available 5446.00 6314.00 5264.00 6118.00 #> Times chosen 1946.00 358.00 1522.00 3174.00 #> Percentage chosen overall 27.80 5.11 21.74 45.34 #> Percentage chosen when available 35.73 5.67 28.91 51.88 #> #> Pre-processing likelihood function... #> #> Testing influence of parameters........ #> Starting main estimation #> Initial function value: -8196.021 #> Initial gradient value: #> asc_bus asc_air asc_rail b_tt_car b_tt_bus b_tt_air #> -1599.667 -39.000 1302.667 92447.083 -597397.500 -1820.833 #> b_tt_rail b_c #> 189332.083 8862.500 #> initial value 8196.020532 #> iter 2 value 7456.688616 #> iter 3 value 7375.154804 #> iter 4 value 7260.844458 #> iter 5 value 6670.816185 #> iter 6 value 6618.448409 #> iter 7 value 6471.224869 #> iter 8 value 6429.069421 #> iter 9 value 6216.357850 #> iter 10 value 6052.604883 #> iter 11 value 5838.718102 #> iter 12 value 5815.462110 #> iter 13 value 5804.515623 #> iter 14 value 5802.299705 #> iter 15 value 5802.031103 #> iter 16 value 5802.023289 #> iter 17 value 5802.022828 #> iter 17 value 5802.022826 #> iter 17 value 5802.022826 #> final value 5802.022826 #> converged #> Additional convergence test using scaled estimation. Parameters will be #> scaled by their current estimates and additional iterations will be #> performed. #> initial value 5802.022826 #> iter 1 value 5802.022826 #> final value 5802.022826 #> converged #> Estimated parameters: #> Estimate #> asc_car 0.000000 #> asc_bus 0.011545 #> asc_air -0.649537 #> asc_rail -1.235711 #> b_tt_car -0.010061 #> b_tt_bus -0.016422 #> b_tt_air -0.011831 #> b_tt_rail -0.004780 #> b_c -0.052923 #> #> Computing covariance matrix using analytical gradient. #> 0%....25%....50%....75%.100% #> Negative definite Hessian with maximum eigenvalue: -3.304564 #> Computing score matrix... #> Calculating LL(0) for applicable models... #> Calculating LL(c) for applicable models... #> Calculating LL of each model component... apollo_modelOutput(model) #> Model run using Apollo for R, version 0.2.5 on Linux by david #> www.ApolloChoiceModelling.com #> #> Model name : MNL #> Model description : Simple MNL model on mode choice SP data #> Model run at : 2021-07-30 20:37:41 #> Estimation method : bfgs #> Model diagnosis : successful convergence #> Number of individuals : 500 #> Number of rows in database : 7000 #> Number of modelled outcomes : 7000 #> #> Number of cores used : 1 #> Model without mixing #> #> LL(start) : -8196.021 #> LL(0) : -8196.021 #> LL(C) : -6706.939 #> LL(final) : -5802.023 #> Rho-square (0) : 0.2921 #> Adj.Rho-square (0) : 0.2911 #> AIC : 11620.05 #> BIC : 11674.87 #> #> #> Estimated parameters : 8 #> Time taken (hh:mm:ss) : 00:00:1.09 #> pre-estimation : 00:00:0.38 #> estimation : 00:00:0.26 #> post-estimation : 00:00:0.45 #> Iterations : 20 #> Min abs eigenvalue of Hessian : 3.304564 #> #> Estimates: #> Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0) #> asc_car 0.000000 NA NA NA NA #> asc_bus 0.011545 0.540840 0.02135 0.541641 0.02132 #> asc_air -0.649537 0.269903 -2.40655 0.266912 -2.43352 #> asc_rail -1.235711 0.321846 -3.83945 0.312775 -3.95079 #> b_tt_car -0.010061 6.3867e-04 -15.75317 6.5823e-04 -15.28521 #> b_tt_bus -0.016422 0.001453 -11.30291 0.001480 -11.09960 #> b_tt_air -0.011831 0.002402 -4.92625 0.002370 -4.99136 #> b_tt_rail -0.004780 0.001650 -2.89736 0.001623 -2.94432 #> b_c -0.052923 0.001422 -37.21130 0.001701 -31.11201 #apollo_saveOutput(model) # ####################################################### # #### 7. Postprocessing of results #### # ####################################################### # ### Use the estimated model to make predictions predictions_base = apollo_prediction(model, apollo_probabilities, apollo_inputs) #> Running predictions from model using parameter estimates... #> Predicted aggregated demand at model estimates #> car bus air rail #> Demand 1946 358 1522 3174 #> #> The output from apollo_prediction is a matrix containing the #> predictions at the estimated values. ### Now imagine the cost for rail increases by 10% ### and predict again database$cost_rail = 1.1*database$cost_rail apollo_inputs = apollo_validateInputs() #> Several observations per individual detected based on the value of ID. #> Setting panelData in apollo_control set to TRUE. #> All checks on apollo_control completed. #> All checks on database completed. predictions_new = apollo_prediction(model, apollo_probabilities, apollo_inputs) #> Running predictions from model using parameter estimates... #> Predicted aggregated demand at model estimates #> car bus air rail #> Demand 2132.59 399.33 1645.75 2822.34 #> #> The output from apollo_prediction is a matrix containing the #> predictions at the estimated values. ### Compare predictions change=(predictions_new-predictions_base)/predictions_base ### Not interested in chosen alternative now, ### so drop last column change=change[,-ncol(change)] ### Summary of changes (possible presence of NAs due to ### unavailable alternatives) summary(change) #> ID Observation car bus air #> Min. :0 Min. :0 Min. :0.0000 Min. :0.0000 Min. :0.0000 #> 1st Qu.:0 1st Qu.:0 1st Qu.:0.0725 1st Qu.:0.0738 1st Qu.:0.0704 #> Median :0 Median :0 Median :0.1168 Median :0.1218 Median :0.1100 #> Mean :0 Mean :0 Mean :0.1105 Mean :0.1225 Mean :0.1121 #> 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.1509 3rd Qu.:0.1674 3rd Qu.:0.1517 #> Max. :0 Max. :0 Max. :0.2339 Max. :0.4326 Max. :0.3677 #> NA's :1554 NA's :686 NA's :1736 #> rail #> Min. :-0.3028 #> 1st Qu.:-0.2060 #> Median :-0.1340 #> Mean :-0.1434 #> 3rd Qu.:-0.0723 #> Max. :-0.0022 #> NA's :882 ## MMNL model file example In this section, we present code to estimate a mixed MNL model (MMNL) using the synthetic data included in the package. After estimation, we predict the effect of a 10% increase in the train fares. The utility function of the model remains the same than in the previous example, i.e.: $U_{nsi} = asc_i + \beta_{tt}tt_{nsi} + \beta_{c}*cost_{nsi} + \varepsilon_{nsi}$ Where n indexes individuals, s choice scenarios, and i alternatives. $$asc_i$$ is the alternative specific constant, $$tt_{nsi}$$ is the travel time and $$cost_{nsi}$$ is the cost. $$\varepsilon_{nsi}$$ is an independent identically distributed standard Gumbel error term. $$\beta_{tt}$$ follows a log-normal distribution with the underlying normal having a mean $$\mu_{tt}$$ and standard deviation $$\sigma_{tt}$$. $$asc_i$$, $$\beta_{c}$$, $$\mu_{tt}$$ and $$\sigma_{tt}$$ are parameters to be estimated. The likelihood function of this model for individual $$n$$ is as follows. $L_{n}=\int_{\beta_{tt}}\prod_{s}P_{nsi}f(\beta_{tt})d\beta_{tt}$ Where $$P_{nsi}=\frac{e^{V_{nsi}}}{\sum_{j}e^{V_{nsj}}}$$, $$V_{nsi}=U_{nsi}-\varepsilon_{nsi}$$, and $$f$$ is the probability density function of $$\beta_{tt}$$. As this function does not have an analytical closed form, we estimate it using Monte Carlo integration, i.e.: $L_{n}\approx\frac{1}{R}\sum_{\beta_{tt}^r}\prod_{s}P_{nsi}^r$ Where $$P_{nsi}^r=P_{nsi}(\beta_{tt}^r)$$, with $$\beta_{tt}^r$$ a random draw of $$\beta_{tt}$$ from its distribution $$f$$, and R is a big number. The code is very similar to the previous example, with only sections 1 and 3 changing. * In section 1 we set mixing = TRUE inside apollo_control, and we set nCores = 2 to speed up estimation by using two computing threads (this is not mandatory). * In section 3 we define the mean (b_tt_mu) and standar deviation (b_tt_sigma) of the underlying normal distribution. We then define the type, name and number of draws used. Finally, we construct the random coefficient $$\beta_{tt}$$ inside a function called apollo_randCoeff. We use 500 inter-individual draws that come from a standard normal distribution, which we later transform into log-normals inside apollo_randCoeff. Even though in this case we only use inter-individual draws, note that inter and intra-individuals draws can be used simultaneously. Inter-individual draws capture variability between individuals, while intra-individual draws capture variability within individuals. In terms of the Monte Carlo integration, inter-individual draws are common for all observations from the same individual, while intra-individual draws are different for each observations. In terms of the likelihood function, the use of intra-individual draws would lead to $$L_{n}\approx\prod_{s}\frac{1}{R}\sum_{\beta_{tt}^r}P_{nsi}$$, which is not the case in this model. Estimation of models with mixing is computationally more demanding than models without mixing. Furthermore, using both inter and intra-individual requires large amounts of memory, which can further slow the estimation process. For this reason, this example is not run automatically. Yet, the users may copy and paste the code in a script, and run it themselves. # ####################################################### # #### 1. Definition of core settings # ####################################################### # ### Clear memory rm(list = ls()) ### Load libraries library(apollo) ### Initialise code apollo_initialise() ### Set core controls apollo_control = list( modelName ="MMNL", modelDescr ="Simple MMNL model on mode choice SP data", indivID ="ID", mixing = TRUE, nCores = 2 ) # ####################################################### # #### 2. Data loading #### # ####################################################### # data("apollo_modeChoiceData", package="apollo") database = apollo_modeChoiceData rm(apollo_modeChoiceData) ### Use only SP data database = subset(database,database$SP==1)

### Create new variable with average income
database$mean_income = mean(database$income)

# ####################################################### #
#### 3. Parameter definition                           ####
# ####################################################### #

### Vector of parameters, including any that are kept fixed
### during estimation
apollo_beta=c(asc_car  = 0,
asc_bus  =-2,
asc_air  =-1,
asc_rail =-1,
mu_tt    =-4,
sigma_tt = 0,
b_c      = 0)

### Vector with names (in quotes) of parameters to be
###  kept fixed at their starting value in apollo_beta.
### Use apollo_beta_fixed = c() for no fixed parameters.
apollo_fixed = c("asc_car")

### Set parameters for generating draws
apollo_draws = list(
interDrawsType = "halton",
interNDraws    = 500,
interUnifDraws = c(),
interNormDraws = c("draws_tt")
)

### Create random parameters
apollo_randCoeff = function(apollo_beta, apollo_inputs){
randcoeff = list()

randcoeff[["b_tt"]] = -exp(mu_tt + sigma_tt*draws_tt)

return(randcoeff)
}

# ####################################################### #
#### 4. Input validation                               ####
# ####################################################### #

apollo_inputs = apollo_validateInputs()

# ####################################################### #
#### 5. Likelihood definition                          ####
# ####################################################### #

apollo_probabilities=function(apollo_beta, apollo_inputs,
functionality="estimate"){

### Attach inputs and detach after function exit
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))

### Create list of probabilities P
P = list()

### List of utilities: these must use the same names as
### in mnl_settings, order is irrelevant.
V = list()
V[['car']]  = asc_car  + b_tt*time_car  + b_c*cost_car
V[['bus']]  = asc_bus  + b_tt*time_bus  + b_c*cost_bus
V[['air']]  = asc_air  + b_tt*time_air  + b_c*cost_air
V[['rail']] = asc_rail + b_tt*time_rail + b_c*cost_rail

### Define settings for MNL model component
mnl_settings = list(
alternatives  = c(car=1, bus=2, air=3, rail=4),
avail         = list(car=av_car, bus=av_bus,
air=av_air, rail=av_rail),
choiceVar     = choice,
V             = V
)

### Compute probabilities using MNL model
P[['model']] = apollo_mnl(mnl_settings, functionality)

### Take product across observation for same individual
P = apollo_panelProd(P, apollo_inputs, functionality)

### Average draws
P = apollo_avgInterDraws(P, apollo_inputs, functionality)

### Prepare and return outputs of function
P = apollo_prepareProb(P, apollo_inputs, functionality)
return(P)
}

# ####################################################### #
#### 6. Model estimation and reporting                 ####
# ####################################################### #

model = apollo_estimate(apollo_beta, apollo_fixed,
apollo_probabilities,
apollo_inputs,
list(writeIter=FALSE))

apollo_modelOutput(model)

#apollo_saveOutput(model)

# ####################################################### #
#### 7. Postprocessing of results                      ####
# ####################################################### #

### Use the estimated model to make predictions
predictions_base = apollo_prediction(model,
apollo_probabilities,
apollo_inputs)

### Now imagine the cost for rail increases by 10%
### and predict again
database$cost_rail = 1.1*database$cost_rail
apollo_inputs   = apollo_validateInputs()
predictions_new = apollo_prediction(model,
apollo_probabilities,
apollo_inputs)

### Compare predictions
change=(predictions_new-predictions_base)/predictions_base
### Not interested in chosen alternative now,
### so drop last column
change=change[,-ncol(change)]
### Summary of changes (possible presence of NAs due to
### unavailable alternatives)
summary(change)

## References

• Ben-Akiva, M. and Lerman, S. (1985) Discrete Choice Analysis. Cambridge, Massachusetts. The MIT Press. ISBN 978-0-262-02217-0
• Ben-Akiva, M.; McFadden, D.; Train, K.; Walker, J.; Bhat, C.; Bierlaire, M.; Bolduc, D.; Boersch-Supan, A.; Brownstone, D.; Bunch, D.; Daly, A.; De Palma, A.; Gopinath, D.; Karlstrom, A.; Munizaga, M. (2002) Hybrid Choice Models: Progress and Challenges. Marketing Letters 13, 163 - 175.
• Bhat, C. (2008) The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations,and model extensions. Transportation Research 42B, 274 - 303.
• Hancock, T.; Hess, S. and Choudhury, C. (2018) Decision field theory: Improvements to current methodology and comparisons with standard choice modelling techniques. Transportation Research 107B, 18-40.
• Pinjari, A. and Bhat, C. (2010) A multiple discrete–continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays. Transportation Research 44B, 562 - 583.
• Train, K. (2009) Discrete Choice Methods with Simulation, 2nd edition. New York, New York. Cambridge University Press. ISBN 978-0-521-76655-5