v03_model_fitting.R
In RprobitB: Bayesian Probit Choice Modeling

## ----label = setup, 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)

## ----eval = FALSE-------------------------------------------------------------
#  fit_model(data = data)

## ----echo = FALSE-------------------------------------------------------------
set.seed(1)

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

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

## ----plot-coef-model-train----------------------------------------------------
plot(coef(model_train), sd = 3)

## ----str-gibbs-samples--------------------------------------------------------
str(model_train$gibbs_samples, max.level = 2, give.attr = FALSE)

## ----summary-model-train------------------------------------------------------
summary(model_train,
  FUN = c(
    "mean" = mean,
    "sd" = stats::sd,
    "R^" = R_hat,
    "custom_stat" = function(x) abs(mean(x) - median(x))
  )
)

## ----plot-trace-model-train---------------------------------------------------
par(mfrow = c(2, 1))
plot(model_train, type = "trace")

## ----plot-acf-model-train-----------------------------------------------------
par(mfrow = c(2, 3))
plot(model_train, type = "acf")

## ----transform-model-train----------------------------------------------------
model_train <- transform(model_train, B = 1)

## ----eval = FALSE-------------------------------------------------------------
#  model_train <- transform(model_train, Q = 100)

## ----eval = FALSE-------------------------------------------------------------
#  model_train <- transform(model_train, scale = "Sigma_1 := 1")