apollo (version 0.0.1)

apollo_modeloutput: Prints estimation results to console

Description

Prints estimation results to console. Amount of information presented can be adjusted through arguments.

Usage

apollo_modeloutput(model, printClassical = TRUE, printPVal = FALSE,
  printT1 = FALSE, printDiagnostics = TRUE, printCovar = FALSE,
  printCorr = FALSE, printOutliers = FALSE, printChange = FALSE)

Arguments

model

Model object. An estimated model as returned by apollo_estimate

printClassical

Boolean. TRUE (default) for printing classical standard errors.

printPVal

Boolean. TRUE for printing p-values. FALSE by default.

printT1

Boolean. TRUE for printing t-test for H0: apollo_beta=1 in addition to H0: apollo_beta=0. FALSE by default.

printDiagnostics

Boolean. TRUE (default) for printing summary of choices in database and other diagnostics.

printCovar

Boolean. TRUE (default) for printing parameter covariance matrix. If printClassical=TRUE, both the classical and robust covariance matrices are printed.

printCorr

Boolean. TRUE (default) for printing parameter correlation matrix. If printClassical=TRUE, both the classical and robust correlation matrices are printed.

printOutliers

Boolean. TRUE (default) for printing 20 individuals with worst average fit across observations.

printChange

Boolean. TRUE (default) for printing difference between starting values and estimates.

Value

A matrix of coefficients, s.d. and t-tests (invisible)

Details

Prints to screen the output of a model previously estimated by apollo_estimate()

Examples

# NOT RUN {
### Set core controls
apollo_control = list(
  modelName ="MNL", # Make sure to use a new name for every model
  indivID   ="ID",  # Name of column in the database with each individual's ID
  mixing    = FALSE,# TRUE for models that include random parameters
  nCores    = 1     # Number of cores to use in estimation
)

### Load data
data(apollo_modeChoiceData)

### Model parameters
apollo_beta = c(asc_1=0, asc_2=0,
                asc_3=0, asc_4=0,
                tt   =0, tc   =0,
                acc  =0)

### Name of parameters fixed to starting values.
apollo_beta_fixed = c("asc_2")

### Likelihood function (do not change the arguments)
### b contains the parameters, x contains the explanatory variables
apollo_probabilities=function(b, x, functionality="estimate"){
  P <- list() ### Do not delete. Store probabilities here.

  ### Enumerate alternatives and availability, and select choice variable.
  alternatives = c(car=1, bus=2, air=3, rail=4)
  avail        = list(car=x$av_car, bus=x$av_bus, air=x$av_air, rail=x$av_rail)
  choiceVar    = x$choice

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

  ### Compute choice probabilities using MNL model
  P[['model']] = apollo_mnl(alternatives, avail, choiceVar, V, functionality)

  return(P)
}

### Estimate model
model = apollo_estimate(apollo_beta, apollo_beta_fixed, database,
                        apollo_probabilities, apollo_control)

### Show output in screen
apollo_modeloutput(model)

# }