# Examples

# An MNL design

    #
    # Example file for creating a simple MNL design
    #
    rm(list = ls(all = TRUE))
    # library(spdesign)

    # Define the list of utility functions ----
    #' Specifying a utility function with 3 attributes and a constant for the
    #' SQ alternative. The design has 20 rows.
    utility <- list(
      alt1 = "b_x1[0.1]  * x1[1:5] + b_x2[0.4] * x2[c(0, 1)] + b_x3[-0.2] * x3[seq(0, 1, 0.25)]",
      alt2 = "b_x1       * x1      + b_x2      * x2          + b_x3       * x3",
      alt3 = "b_sq[0.15] * sq[1]"
    )

    # Generate designs ----
    design <- generate_design(utility, rows = 20,
                              model = "mnl", efficiency_criteria = "d-error",
                              algorithm = "rsc", draws = "scrambled-sobol",
                              control = list(
                                max_iter = 21000,
                                max_no_improve = 5000
                              ))

    # Add a blocking variable to the design with 4 blocks.
    design <- block(design, 4)

    summary(design)

# An MNL design with interactions

    #
    # Example file for creating a simple MNL design
    #
    rm(list = ls(all = TRUE))
    # library(spdesign)

    # Define the list of utility functions ----
    utility <- list(
      alt1 = "b_x1[0.1] * x1[1:5] + b_x2[0.4] * x2[c(0, 1)] + b_x3[-0.2] * x3[seq(0, 1, 0.25)] + b_x1x2[-0.1] * I(x1 * x2)",
      alt2 = "b_x1      * x1      + b_x2      * x2          + b_x3       * x3"
    )

    # Generate designs ----
    design <- generate_design(utility, rows = 20,
                              model = "mnl", efficiency_criteria = "d-error",
                              algorithm = "federov", draws = "scrambled-sobol",
                              dudx = "b_x3")

    summary(design)

# An MNL design with multiway interactions

    #
    # Example file for creating a simple MNL design
    #
    rm(list = ls(all = TRUE))
    # library(spdesign)

    # Define the list of utility functions ----
    utility <- list(
      alt1 = "b_x1[0.1] * x1[1:5] + b_x2[0.4] * x2[c(0, 1)] + b_x3[-0.2] * x3[seq(0, 1, 0.25)] + b_x1x2[-0.1] * I(x1 * x2)",
      alt2 = "b_x1      * x1      + b_x2      * x2          + b_x3       * x3 + b_x1x2x3[0.1] * I(x1 * x2 * x3)"
    )

    # Generate designs ----
    design <- generate_design(utility, rows = 20,
                              model = "mnl", efficiency_criteria = "d-error",
                              algorithm = "rsc", draws = "scrambled-sobol",
                              dudx = "b_x3")

    summary(design)

# An MNL design with dummy-coded attributes

    #
    # Example file for creating a simple MNL design
    #
    rm(list = ls(all = TRUE))
    # library(spdesign)

    # Define the list of utility functions ----
    #' A design where the first attribute is dummy-coded.
    utility <- list(
      alt1 = "b_x1_dummy[c(0.1, 0.2)] * x1[c(1, 2, 3)] + b_x2[0.4] * x2[c(0, 1)] + b_x3[-0.2] * x3[seq(0, 1, 0.25)]",
      alt2 = "b_x1_dummy              * x1             + b_x2      * x2          + b_x3       * x3"
    )

    # Generate designs ----
    design <- generate_design(utility, rows = 20,
                              model = "mnl", efficiency_criteria = "d-error",
                              algorithm = "rsc", draws = "scrambled-sobol")

    # Add a blocking variable to the design with 2 blocks.
    design <- block(design, 2)

    summary(design)

# An MNL design with specified level occurrences

    #
    # Example file for creating a simple MNL design
    #
    rm(list = ls(all = TRUE))
    # library(spdesign)

    # Define the list of utility functions ----
    utility <- list(
      alt1 = "b_x1_dummy[c(0.1, 0.2)] * x1[c(1, 2, 3)](4:14) + b_x2[0.4] * x2[c(0, 1)](9:11) + b_x3[-0.2] * x3[seq(0, 1, 0.25)]",
      alt2 = "b_x1_dummy              * x1                   + b_x2      * x2                + b_x3       * x3"
    )

    # Generate designs ----
    design <- generate_design(utility, rows = 20,
                              model = "mnl", efficiency_criteria = "d-error",
                              algorithm = "federov", draws = "scrambled-sobol")

    design <- block(design, 4)

    summary(design)

# An MNL design with alternative specific attributes

    #
    # Example file for creating a simple MNL design
    #
    rm(list = ls(all = TRUE))
    # library(spdesign)

    # Define the list of utility functions ----
    utility <- list(
      car   = "                          b_travel_time[-0.1] * travel_time_car[c(10, 15, 20, 25)]   + b_travel_cost[-0.2] * travel_cost_car[c(25, 50, 75, 100)]  + b_comfort_dummy[c(0.1, 0.2)] * comfort[c(1, 2, 3)]",
      bus   = "b_bus[-0.1]  * bus[1]   + b_travel_time       * travel_time_bus[c(20, 25, 30, 35)]   + b_travel_cost       * travel_cost_bus[c(10, 15, 20, 25)]   + b_comfort_dummy              * comfort",
      train = "b_train[0.1] * train[1] + b_travel_time       * travel_time_train[c(15, 20, 25, 30)] + b_travel_cost       * travel_cost_train[c(20, 30, 40, 50)] + b_comfort_dummy              * comfort"
    )

    # Generate designs ----
    design <- generate_design(utility, rows = 20,
                              model = "mnl", efficiency_criteria = "d-error",
                              algorithm = "rsc", draws = "scrambled-sobol",
                              control = list(
                                max_iter = 21000,
                                efficiency_threshold = 0.01
                              ))

    # Add a blocking variable to the design with 4 blocks.
    design <- block(design, 4)

    summary(design)

# An MNL design with Bayesian priors

    #
    # Example file for creating a simple MNL design with Bayesian priors
    #
    rm(list = ls(all = TRUE))
    # library(spdesign)

    # Define the list of utility functions ----
    #' Generating a design with Bayesian priors.
    utility <- list(
      alt1 = "b_x1[0.1] * x1[2:5] + b_x2[uniform_p(-1, 1)] * x2[c(0, 1)] + b_x3[normal_p(0, 1)] * x3[seq(0, 1, 0.25)]",
      alt2 = "b_x1      * x1      + b_x2                   * x2          + b_x3                 * x3"
    )

    # Generate designs ----
    design <- generate_design(utility, rows = 20,
                              model = "mnl", efficiency_criteria = "d-error",
                              algorithm = "rsc", draws = "scrambled-sobol",
                              control = list(
                                max_iter = 10000
                              ))

    summary(design)

# An MNL design with dummy-coded attributes, Bayesian priors, and restrictions on level occurrence

    #
    # Example file for creating a simple MNL design with Bayesian priors
    #
    rm(list = ls(all = TRUE))
    # library(spdesign)

    # Define the list of utility functions ----
    #' Generating a design with Bayesian priors using dummy coding and level occurrences
    #' NOTE: This design may take a long time. It has to first find a candidate that
    #' meets the restrictions and then evaluate whether it is better than the
    #' current best. Little information is provided about the process along the way.
    utility <- list(
      alt1 = "b_x1_dummy[c(uniform_p(-1, 1), uniform_p(-1, 1))] * x1[c(1, 2, 3)](6:10, 4:14, 6:10) + b_x2[0.4] * x2[c(0, 1)](9:11) + b_x3[-0.2] * x3[seq(0, 1, 0.25)]",
      alt2 = "b_x1_dummy                                        * x1                               + b_x2      * x2                + b_x3       * x3"
    )

    # Generate designs ----
    design <- generate_design(utility, rows = 20,
                              model = "mnl", efficiency_criteria = "d-error",
                              algorithm = "random", draws = "scrambled-sobol",
                              control = list(
                                max_iter = 10000
                              ))

    summary(design)

# An MNL design with a supplied candidate set

    #
    # Example file for creating a simple MNL design
    #
    rm(list = ls(all = TRUE))
    # library(spdesign)

    # Define the list of utility functions ----
    utility <- list(
      alt1 = "b_x1[0.1] * x1[2:5] + b_x2[0.4] * x2[c(0, 1)] + b_x3[-0.2] * x3[seq(0, 1, 0.25)]",
      alt2 = "b_x1      * x1      + b_x2      * x2          + b_x3       * x3",
      alt3 = "b_sq[0]   * sq[1]"
    )

    # Use the full factorial as the candidate set
    candidate_set <- full_factorial(
      list(
        alt1_x1 = 2:5,
        alt1_x2 = c(0, 1),
        alt1_x3 = seq(0, 1, 0.25),
        alt2_x1 = 2:5,
        alt2_x2 = c(0, 1),
        alt2_x3 = seq(0, 1, 0.25),
        alt3_sq = 1
      )
    )

    candidate_set <- candidate_set[!(candidate_set$alt1_x1 == 2 & candidate_set$alt1_x2 == 0 & candidate_set$alt1_x3 == 0), ]
    candidate_set <- candidate_set[!(candidate_set$alt2_x2 == 1 & candidate_set$alt2_x3 == 1), ]

    # Generate designs ----
    design <- generate_design(utility, rows = 20,
                              model = "mnl", efficiency_criteria = "d-error",
                              algorithm = "federov", draws = "scrambled-sobol",
                              candidate_set = candidate_set)

    summary(design)

# An MNL design with specified explusions applied to the candidate set

    #
    # Example file for creating a simple MNL design
    #
    rm(list = ls(all = TRUE))
    # library(spdesign)

    # Define the list of utility functions ----
    utility <- list(
      alt1 = "b_x1[0.1] * x1[2:5] + b_x2[0.4] * x2[c(0, 1)] + b_x3[-0.2] * x3[seq(0, 1, 0.25)]",
      alt2 = "b_x1      * x1      + b_x2      * x2          + b_x3       * x3"
    )

    # Generate designs ----
    design <- generate_design(utility, rows = 20,
                              model = "mnl", efficiency_criteria = "d-error",
                              algorithm = "federov", draws = "scrambled-sobol",
                              exclusions = list(
                                "alt1_x1 == 2 & alt1_x2 == 0 & alt1_x3 == 0",
                                "alt2_x2 == 1 & alt2_x3 == 1"
                              ))

# An MNL design optimized for c-efficiency

    #
    # Example file for creating a simple MNL design
    #
    rm(list = ls(all = TRUE))
    # library(spdesign)

    # Define the list of utility functions ----
    #' Specifying a utility function with 3 attributes and a constant for the
    #' SQ alternative. The design has 20 rows.
    utility <- list(
      alt1 = "b_x1[0.1]  * x1[1:5] + b_x2[0.4] * x2[c(0, 1)] + b_x3[-0.2] * x3[seq(0, 1, 0.25)]",
      alt2 = "b_x1       * x1      + b_x2      * x2          + b_x3       * x3",
      alt3 = "b_sq[0.15] * sq[1]"
    )

    # Generate designs ----
    design <- generate_design(utility, rows = 20,
                              dudx = "b_x3",
                              model = "mnl",
                              efficiency_criteria = "c-error",
                              algorithm = "rsc",
                              draws = "scrambled-sobol",
                              control = list(
                                max_iter = 21000,
                                max_no_improve = 5000
                              ))

    # Add a blocking variable to the design with 4 blocks.
    design <- block(design, 4)

    summary(design)