v05_choice_prediction.R
In RprobitB: Bayesian Probit Choice Modeling

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "img/",
  fig.align = "center",
  fig.dim = c(8, 6), 
  out.width = "75%"
)
library("RprobitB")
options("RprobitB_progress" = FALSE)

## ---- echo = FALSE------------------------------------------------------------
set.seed(1)
data("Train", package = "mlogit")
Train$price_A <- Train$price_A / 100 * 2.20371
Train$price_B <- Train$price_B / 100 * 2.20371
Train$time_A <- Train$time_A / 60
Train$time_B <- Train$time_B / 60

## ---- message = FALSE---------------------------------------------------------
form <- choice ~ price + time + change + comfort | 0
data <- prepare_data(form = form, choice_data = Train)
model_train <- fit_model(
  data = data,
  scale = "price := -1"
)

## ---- predict-model-train-----------------------------------------------------
predict(model_train)

## ---- predict-model-train-indlevel--------------------------------------------
pred <- predict(model_train, overview = FALSE) 
head(pred, n = 10)

## ---- model-train-covs--------------------------------------------------------
get_cov(model_train, id = 1, idc = 8)

## ---- model-train-coeffs------------------------------------------------------
coef(model_train)

## ---- model-train-Sigma-------------------------------------------------------
point_estimates(model_train)$Sigma

## ---- roc-example, warning = FALSE, message = FALSE, out.width = "50%", fig.dim = c(6,6)----
library(plotROC)
ggplot(data = pred, aes(m = A, d = ifelse(true == "A", 1, 0))) + 
  geom_roc(n.cuts = 20, labels = FALSE) + 
  style_roc(theme = theme_grey)

## ---- predict-model-train-given-covs-1----------------------------------------
predict(
  model_train, 
  data = data.frame("price_A" = c(100,110), 
                    "price_B" = c(100,100)),
  overview = FALSE)

## ---- predict-model-train-given-covs-2----------------------------------------
predict(
  model_train, 
  data = data.frame("price_A"   = c(100,110), 
                    "comfort_A" = c(1,0),
                    "price_B"   = c(100,100),
                    "comfort_B" = c(1,1)),
  overview = FALSE)