View source: R/data_management.R
simulate_choices | R Documentation |
This function simulates choice data from a probit model.
simulate_choices(
form,
N,
T = 1,
J,
re = NULL,
alternatives = NULL,
ordered = FALSE,
ranked = FALSE,
base = NULL,
covariates = NULL,
seed = NULL,
true_parameter = list()
)
form |
A
Multiple covariates (of one type) are separated by a In the ordered probit model ( |
N |
The number (greater or equal 1) of decision makers. |
T |
The number (greater or equal 1) of choice occasions or a vector of choice
occasions of length |
J |
The number (greater or equal 2) of choice alternatives. |
re |
A character (vector) of covariates of |
alternatives |
A character vector with the names of the choice alternatives.
If not specified, the choice set is defined by the observed choices.
If |
ordered |
A boolean, |
ranked |
TBA |
base |
A character, the name of the base alternative for covariates that are not
alternative specific (i.e. type 2 covariates and ASCs). Ignored and set to
|
covariates |
A named list of covariate values. Each element must be a vector of length equal to the number of choice occasions and named according to a covariate. Covariates for which no values are supplied are drawn from a standard normal distribution. |
seed |
Set a seed for the simulation. |
true_parameter |
Optionally specify a named list with true parameter values for |
See the vignette on choice data for more details.
An object of class RprobitB_data
.
check_form()
for checking the model formula
overview_effects()
for an overview of the model effects
create_lagged_cov()
for creating lagged covariates
as_cov_names()
for re-labeling alternative-specific covariates
prepare_data()
for preparing empirical choice data
train_test()
for splitting choice data into a train and test subset
### simulate data from a binary probit model with two latent classes
data <- simulate_choices(
form = choice ~ cost | income | time,
N = 100,
T = 10,
J = 2,
re = c("cost", "time"),
alternatives = c("car", "bus"),
seed = 1,
true_parameter = list(
"alpha" = c(-1, 1),
"b" = matrix(c(-1, -1, -0.5, -1.5, 0, -1), ncol = 2),
"C" = 2
)
)
### simulate data from an ordered probit model
data <- simulate_choices(
form = opinion ~ age + gender,
N = 10,
T = 1:10,
J = 5,
alternatives = c("very bad", "bad", "indifferent", "good", "very good"),
ordered = TRUE,
covariates = list(
"gender" = rep(sample(c(0, 1), 10, replace = TRUE), times = 1:10)
),
seed = 1
)
### simulate data from a ranked probit model
data <- simulate_choices(
form = product ~ price,
N = 10,
T = 1:10,
J = 3,
alternatives = c("A", "B", "C"),
ranked = TRUE,
seed = 1
)
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