Introduction

The code is broken down into series of steps, it very slightly deviates from the manual in step 2 because we will use tidyverse package to read, analyze and preprocess data since it is more flexible and very useful tool for future.

  • Step 1 - Initialisation

    • set working directory
    • set apollo_control

  • Step 2 - Data

    • read file (csv or spss)
    • clean data (optional)
    • if we read spss file, remove labels/label/attributes
    • convert tibble to dataframe

  • Step 3 - Define model parameters

    • initialise parameters

  • Step 4 - Validate Inputs

  • Step 5 - Define apollo probabilities

    • define utility equation
    • set ol_settings

  • Step 6 - Estimate model

  • Step 7 - Print and save output

# --------------------------------------------------------------------------------
### Step 1 - Initialise the code 

# clean workspace
rm(list = ls())

# (OPTIONAL) set working directory
setwd("C:/Users/gulhare.s/Dropbox (UFL)/PhD/Discrete choice modeling/Assignments/Assignment 3")

# load libraries
library(tidyverse) # for data science
library(haven) # to read SPSS files
library(apollo) # for mode choice analysis

# mandatory step
apollo_initialise()

#### Step 1-2 - Set Apollo controls
apollo_control = list(
  modelName  = "Model_OL",
  modelDescr ="Model ordered logit",
  indivID    ="CASE"
)

# --------------------------------------------------------------------------------
### Step 2 - Data

# read SPSS file
tbl <- read_csv("Assign3.csv") %>% 
  mutate(stops = case_when(
    WHCSTOPS == 0 ~ "never",
    WHCSTOPS == 1 ~ "one",
    WHCSTOPS == 2 ~ "two",
    WHCSTOPS == 3 ~ "three",
    WHCSTOPS == 4 ~ "four"),
    stops_factor = factor(stops, ordered = TRUE, 
                          levels = c("never", "one", "two", "three", "four")))


# convert tibble to dataframe, always use variable name database
database <- as.data.frame(tbl)


# --------------------------------------------------------------------------------
### Step 3 - Define model parameters

### Vector of parameters, including any that are kept fixed in estimation
apollo_beta = c(b_dist  = 0,
                
                tau_1   = 1, 
                tau_2   = 2, 
                tau_3   = 3, 
                tau_4   = 4)

### Vector with names (in quotes) of parameters to be kept fixed at their starting value in apollo_beta, use apollo_beta_fixed = c() if none
apollo_fixed = c()

# --------------------------------------------------------------------------------
### Step 4 - Validate data
apollo_inputs = apollo_validateInputs()

# --------------------------------------------------------------------------------
### Step 5 - Define apollo probabilities

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))
  
  ### Utility equation
  V = b_dist * DISTANCE
  
  ### Create list of probabilities P
  P = list()
  
  ### Define settings for MNL model component
  ol_settings = list(
    outcomeOrdered = WHCSTOPS,
    V              = V,
    tau            = c(tau_1, tau_2, tau_3, tau_4),
    coding         = c(0, 1, 2, 3, 4))

  ### Compute probabilities using MNL model,
  P[['model']] = apollo_ol(ol_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)
}
## Optional: calculate LL before model estimation
apollo_llCalc(apollo_beta, apollo_probabilities, apollo_inputs)


# --------------------------------------------------------------------------------
### Step 6 - Estimate model

model = apollo_estimate(apollo_beta, 
                        apollo_fixed, 
                        apollo_probabilities, 
                        apollo_inputs)

# --------------------------------------------------------------------------------
### Step 7 - Model output

# print model output
apollo_modelOutput(model)