pIMCE: Population-Weighted Heterogeneous Effects Analysis of...

In cjbart: Heterogeneous Effects Analysis of Conjoint Experiments

Population-Weighted Heterogeneous Effects Analysis of Conjoint Results

Description

pIMCE calculates the population individual-level marginal component effects from a BART-estimated conjoint model, using marginal attribute distributions specified by the researcher.

Usage

pIMCE(
  model,
  covar_data,
  attribs,
  l,
  l_1,
  l_0,
  marginals,
  method = "bayes",
  alpha = 0.05,
  cores = 1,
  skip_checks = FALSE,
  verbose = TRUE
)

Arguments

model	A model object, the result of running cjbart()
covar_data	A data.frame of covariate information to predict pIMCEs over
attribs	Vector of attribute names
l	Name of the attribute of interest
l_1	Attribute-level of interest for attribute l
l_0	Reference level for attribute l
marginals	A named list where every element is a named vector of marginal probabilities for each corresponding attribute-level. For example, marginals = list("A1" = c("q" = 0.4, "r" = 0.6), "A2" = c("x" = 0.7, "y" = 0.2, "z" = 0.1))
method	Character string, setting the variance estimation method to use. When method is "parametric", a typical combined variance estimate is employed; when method = "bayes", the 95% posterior interval is calculated; and when method = "rubin", combination rules are used to combine the variance analogous to in multiple imputation analysis.
alpha	Number between 0 and 1 – the significance level used to compute confidence/posterior intervals. When method = "bayes", the posterior interval is calculated by taking the alpha/2 and (1-alpha/2) quantiles of the posterior draws. When method = "rubin", the confidence interval equals the IMCE +/- qnorm(alpha/2). By default, alpha is 0.05 i.e. generating a 95% confidence/posterior interval.
cores	Number of CPU cores used during prediction phase
skip_checks	Boolean, indicating whether to check the structure of the data (default = FALSE). Only set this to TRUE if you are confident that the data is structured appropriately
verbose	Boolean, indicating whether to print progress (default = TRUE)

Details

This function calculates the population-weighted IMCE, which takes into account the population distribution of profiles. Rather than average over the multiple OMCE estimates, this function generates estimated treatment effects for all possible potential outcomes along all attributes except the attribute of interest, and then marginalizes these over the supplied marginal distributions. Uncertainty estimates are recovered using credible intervals.

Value

pIMCE returns a data.frame of population-weighted estimates, credible interval bounds, and the covariate information supplied

See Also

cjbart()

cjbart documentation built on Sept. 8, 2023, 5:57 p.m.