apollo (version 0.0.8)

apollo_mnl: Calculates multinomial logit probabilities

Description

Calculates probabilities of a multinomial logit model.

Usage

apollo_mnl(mnl_settings, functionality)

Arguments

mnl_settings

List of inputs of the MNL model. It should contain the following.

• alternatives: Named numeric vector. Names of alternatives and their corresponding value in choiceVar.

• avail: Named list of numeric vectors or scalars. Availabilities of alternatives, one element per alternative. Names of elements must match those in alternatives. Values can be 0 or 1.

• choiceVar: Numeric vector. Contains choices for all observations. It will usually be a column from the database. Values are defined in alternatives.

• V: Named list of deterministic utilities . Utilities of the alternatives. Names of elements must match those in alternatives.

• rows: Boolean vector. Consideration of rows in the likelihood calculation, FALSE to exclude. Length equal to the number of observations (nObs). Default is "all", equivalent to rep(TRUE, nObs).

functionality

Character. Can take different values depending on desired output.

• "estimate": Used for model estimation.

• "prediction": Used for model predictions.

• "validate": Used for validating input.

• "zero_LL": Used for calculating null likelihood.

• "conditionals": Used for calculating conditionals.

• "output": Used for preparing output after model estimation.

• "raw": Used for debugging.

Value

The returned object depends on the value of argument functionality as follows.

• "estimate": vector/matrix/array. Returns the probabilities for the chosen alternative for each observation.

• "prediction": List of vectors/matrices/arrays. Returns a list with the probabilities for all alternatives, with an extra element for the chosen alternative probability.

• "validate": Boolean. Returns TRUE if all tests are passed.

• "zero_LL": vector/matrix/array. Returns the probability of the chosen alternative when all parameters are zero.

• "conditionals": Same as "prediction".

• "output": Same as "estimate" but also writes summary of choices into temporary file (later read by apollo_modelOutput).

• "raw": Same as "prediction".

Examples

# NOT RUN {
### Load data
data(apollo_modeChoiceData)
database <- apollo_modeChoiceData
rm(apollo_modeChoiceData)

### Parameters
b = list(asc_1=0, asc_2=0, asc_3=0, asc_4=0, tt=0, tc=0, acc=0)

### List of utilities
V = list()
V[['car' ]] = b$asc_1 + b$tt*database$time_car  + b$tc*database$cost_car
V[['bus' ]] = b$asc_2 + b$tt*database$time_bus  + b$tc*database$cost_bus  +
              b$acc*database$access_bus
V[['air' ]] = b$asc_3 + b$tt*database$time_air  + b$tc*database$cost_air  +
              b$acc*database$access_air
V[['rail']] = b$asc_4 + b$tt*database$time_rail + b$tc*database$cost_rail +
              b$acc*database$access_rail

### MNL settings
mnl_settings <- list(
   alternatives = c(car=1, bus=2, air=3, rail=4),
   avail        = list(car=database$av_car, bus=database$av_bus,
                       air=database$av_air, rail=database$av_rail),
   choiceVar    = database$choice,
   V            = V
)

### Compute choice probabilities using MNL model
apollo_mnl(mnl_settings, functionality="estimate")
# }