#
# 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)#
# 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)#
# 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)#
# 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)#
# 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)#
# 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)#
# 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)#
# 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)#
# 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)#
# 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"
))#
# 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)