cjbart {cjbart} | R Documentation |
Generate Conjoint Model Using BART
Description
A wrapper for the BART::pbart()
function used for estimating heterogeneity in conjoint models
Usage
cjbart(
data,
Y,
type = NULL,
id = NULL,
round = NULL,
use_round = TRUE,
cores = 1,
...
)
Arguments
data |
A data.frame, containing all attributes, controls, the outcome and id variables to analyze. |
Y |
Character string – the outcome variable |
type |
Type of conjoint experiment – either "choice" (for forced-choice outcomes) or "rating" (for interval ratings). If NULL (default), the function will attempt to automatically detect the outcome type. |
id |
Character string – variable identifying individual respondents (optional) |
round |
Character string – variable identifying rounds of the conjoint experiment |
use_round |
Boolean – whether to include the round indicator column when training the BART model (default = |
cores |
Integer – number of CPU cores used in model training |
... |
Other arguments passed to |
Details
Please note, Windows users cannot use the parallelized BART::mc.pbart()
function, and so setting an internal seed will not be used.
Value
A trained BART::pbart()
model that can be passed to IMCE()
See Also
Examples
subjects <- 5
rounds <- 2
profiles <- 2
obs <- subjects*rounds*profiles
fake_data <- data.frame(A = sample(c("a1","a2"), obs, replace = TRUE),
B = sample(c("b1","b2"), obs, replace = TRUE),
id1 = rep(1:subjects, each=rounds),
stringsAsFactors = TRUE)
fake_data$Y <- sample(c(0,1), obs, replace = TRUE)
cj_model <- cjbart(data = fake_data,
Y = "Y",
id = "id1")