Title:	Heterogeneous Effects Analysis of Conjoint Experiments
Version:	0.3.2
Description:	A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.
License:	Apache License (≥ 2.0)
Encoding:	UTF-8
RoxygenNote:	7.2.3
Depends:	R (≥ 3.6.0), BART
Imports:	stats, rlang, tidyr, ggplot2, randomForestSRC (≥ 3.2.2), Rdpack
Suggests:	testthat (≥ 3.0.0), knitr, parallel, rmarkdown
VignetteBuilder:	knitr
URL:	https://github.com/tsrobinson/cjbart
BugReports:	https://github.com/tsrobinson/cjbart/issues
RdMacros:	Rdpack
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2023-09-06 15:00:59 UTC; tsr
Author:	Thomas Robinson [aut, cre, cph], Raymond Duch [aut, cph]
Maintainer:	Thomas Robinson <ts.robinson1994@gmail.com>
Repository:	CRAN
Date/Publication:	2023-09-06 16:10:08 UTC

Average Marginal Component Effect Estimation with Credible Interval

Description

AMCE calculates the average marginal component effects from a BART-estimated conjoint model.

Usage

AMCE(
  data,
  model,
  attribs,
  ref_levels,
  method = "bayes",
  alpha = 0.05,
  cores = 1,
  skip_checks = FALSE
)

Arguments

Details

The AMCE estimates are the average of all computed OMCEs.

Value

AMCE returns an object of type "cjbart", a list object.

See Also

cjbart()

Heterogeneous Effects Analysis of Conjoint Results

Description

IMCE calculates the individual-level marginal component effects from a BART-estimated conjoint model.

Usage

IMCE(
  data,
  model,
  attribs,
  ref_levels,
  method = "bayes",
  alpha = 0.05,
  keep_omce = FALSE,
  cores = 1,
  skip_checks = FALSE
)

Arguments

Details

The OMCE estimates are the result of subtracting the predicted value of each observation under the reference-level category from the predicted value of each observation under the given attribute level. If an attribute has k levels, then this will yield k-1 estimates per observation. The IMCE is the average of the OMCEs for each individual within the data.

Value

IMCE returns an object of type "cjbart", a list object.

See Also

cjbart()

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")

## Skip if not Unix due to longer CPU time
if (.Platform$OS.type=='unix') {

  het_effects <- IMCE(data = fake_data,
                      model = cj_model,
                      attribs = c("A","B"),
                      ref_levels = c("a1","b1"),
                      cores = 1)

  summary(het_effects)
}

Inspect Round-Level Marginal Component Effect (RMCE)

Description

RMCE calculates the round-level marginal component effects from a cjbart model.

Usage

RMCE(imces)

Arguments

Details

The RMCE estimates are the result of averaging the OMCEs within each round, for each subject in the experiment. The RMCE is the intermediate causal quantity between OMCEs and IMCEs, and can be useful for inspecting whether there are any carryover or stability issues across rounds.

Value

IMCE returns a data frame of RMCEs.

See Also

cjbart() and IMCE()

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

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

BART::pbart()

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")

Estimate Variable Importance Metrics for cjbart Object

Description

Estimates random forest variable importance scores for multiple attribute-levels of a conjoint experiment.

Usage

het_vimp(imces, levels = NULL, covars = NULL, cores = 1, ...)

Arguments

Details

Having generated a schedule of individual-level marginal component effect estimates, this function fits a random forest model for each attribute-level using the supplied covariates as predictors. It then calculates a variable importance measure (VIMP) for each covariate. The VIMP method assesses how important each covariate is in terms of partitioning the predicted individual-level effects distribution, and can thus be used as an indicator of which variables drive heterogeneity in the IMCEs.

To recover a VIMP measure, we used permutation-based importance metrics recovered from random forest models estimated using randomForestSRC::rfsrc(). To permute the data, this function uses random node assignment, whereby cases are randomly assigned to a daughter node whenever a tree splits on the target variable (see Ishwaran et al. 2008). Importance is defined in terms of how random node assignment degrades the performance of the forest. Higher degradation indicates a variable is more important to prediction.

Variance estimates of each variable's importance are subsequently recovered using the delete-d jackknife estimator developed by Ishwaran and Lu (2019). The jackknife method has inherent bias correction properties, making it particularly effective for variable selection exercises such as identifying drivers of heterogeneity.

Value

A "long" data.frame of variable importance scores for each combination of covariates and attribute-levels, as well as the estimated 95% confidence intervals for each metric.

References

Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS (2008). “Random survival forests.” The annals of applied statistics, 2(3), 841–860.

Ishwaran H, Lu M (2019). “Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival.” Statistics in medicine, 38(4), 558–582.

See Also

randomForestSRC::rfsrc() and randomForestSRC::subsample()

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

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()

Plot Marginal Component Effects of a cjbart Object

Description

Plots observation-level or individual-level marginal component effects (OMCE and IMCE respectively). By default, all attribute-levels in the model are plotted.

Usage

## S3 method for class 'cjbart'
plot(x, covar = NULL, plot_levels = NULL, se = TRUE, ...)

Arguments

Value

Plot of marginal component effects.

Plot Variable Importance Matrix for Heterogeneity Analysis

Description

Plots a heatmap of variable importance, across predicted IMCEs. By default, all attribute-levels and covariates in the model are plotted.

Usage

## S3 method for class 'cjbart.vimp'
plot(x, covars = NULL, att_levels = NULL, ...)

Arguments

Value

Plot of covariate importance scores

Estimate a Single Variable Importance Metric for cjbart Object

Description

Estimates random forest variable importance scores for a single attribute-level of a conjoint experiment. This function is for advanced use. Users should typically use the het_vimp() function.

Usage

rf_vimp(model, outcome, covars = NULL)

Arguments

Value

Data.frame of variable importance scores for each covariate in the model, as well as values for the estimated 95% confidence interval for each importance score.

Summarizing cjbart Marginal Component Effect Estimates

Description

summary method for class "cjbart"

Usage

## S3 method for class 'cjbart'
summary(object, ...)

Arguments

Value

Data frame summarizing the average marginal component effect (AMCE), the minimum and maximum values, and standard deviations for each attribute-level.

Note

To calculate the AMCE with Bayesian credible intervals, please use the AMCE() function instead.

See Also

AMCE()