pIMCE calculates the population individual-level marginal component effects from a BART-estimated conjoint model, using marginal attribute distributions specified by the researcher.
pIMCE(
model,
covar_data,
attribs,
l,
l_1,
l_0,
marginals,
method = "bayes",
alpha = 0.05,
cores = 1,
skip_checks = FALSE,
verbose = TRUE
)pIMCE returns a data.frame of population-weighted estimates, credible interval bounds, and the covariate information supplied
A model object, the result of running cjbart()
A data.frame of covariate information to predict pIMCEs over
Vector of attribute names
Name of the attribute of interest
Attribute-level of interest for attribute l
Reference level for attribute l
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))
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.
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.
Number of CPU cores used during prediction phase
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
Boolean, indicating whether to print progress (default = TRUE)
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.
cjbart()