model_pAMCE: Estimating the population AMCE using a model-based approach
In factorEx: Design and Analysis for Factorial Experiments

Description Usage Arguments Value References Examples

model_pAMCE implements the model-based approach to estimate the pAMCE. See de la Cuesta, Egami, and Imai (2019+) for details. More examples are available at the GitHub page of factorEx.

model_pAMCE(
  formula,
  formula_three = NULL,
  data,
  reg = TRUE,
  ord_fac,
  pair = FALSE,
  pair_id = NULL,
  cross_int = FALSE,
  cluster_id = NULL,
  target_dist,
  target_type,
  difference = FALSE,
  cv_type = "cv.1Std",
  nfolds = 5,
  boot = 100,
  seed = 1234,
  numCores = NULL
)

`formula`	Formula
`formula_three`	Formula for three-way interactions (optional)
`data`	Data
`reg`	TRUE (regularization) or FALSE (no regularization). Default is TRUE
`ord_fac`	Whether we assume each factor is ordered. When not specified, we assume all of them are ordered
`pair`	Whether we use a paired-choice conjoint design
`pair_id`	Unique identifiers for pairs in the paired-choice conjoint design (optional)
`cross_int`	Include interactions across profiles. Default is FALSE.
`cluster_id`	Unique identifiers for computing cluster standard errors (optional).
`target_dist`	Target profile distributions to be used. This argument should be 'list'
`target_type`	Types of target profile distributions. 'marginal' or 'target_data'. See Examples for details.
`difference`	Whether we compute the differences between the multiple pAMCEs. Default is FALSE.
`cv_type`	(optimal only when 'reg = TRUE“) 'cv.1Std“ (stronger regularization; default) or 'cv.min' (weaker regularization).
`nfolds`	Number of cross validation folds. Default is 5.
`boot`	The number of bootstrap samples.
`seed`	Seed for bootstrap.
`numCores`	Number of cores to be used for parallel computing. If not specified, detect the number of available cores internally.

model_pAMCE returns an object of pAMCE class.

AMCE: Estimates of the pAMCE for all factors.
boot_AMCE: Estimates of the pAMCE for all factors in each bootstrap sample.
boot_coef: Estimates of coefficients for the linear probability model in each bootstrap sample.
approach: "model_based"
input: Input into the function.
...: Values for internal use.

de la Cuesta, Egami, and Imai. (2019+). Improving the External Validity of Conjoint Analysis: The Essential Role of Profile Distribution. (Working Paper). Available at https://scholar.princeton.edu/sites/default/files/negami/files/conjoint_profile.pdf.

Egami and Imai. (2019). Causal Interaction in Factorial Experiments: Application to Conjoint Analysis. Journal of the American Statistical Association, Vol.114, No.526 (June), pp. 529–540. Available at https://scholar.princeton.edu/sites/default/files/negami/files/causalint.pdf.

  # Small example
  target_dist_marginal <- OnoBurden$target_dist_marginal
  OnoBurden_data <- OnoBurden$OnoBurden_data
  OnoBurden_data_small <- OnoBurden_data[1:300, ]
  target_dist_marginal_small <- target_dist_marginal[c("gender", "race")]

  # model-based estimation without regularization
  out_model_s <-
      model_pAMCE(formula = Y ~ gender + race,
           data = OnoBurden_data_small, reg = FALSE,
           pair_id = OnoBurden_data_small$pair_id,
           cluster_id = OnoBurden_data_small$id,
           target_dist  = target_dist_marginal_small,
           target_type = "marginal")


  # Example
  data("OnoBurden")
  OnoBurden_data <- OnoBurden$OnoBurden_data
  OnoBurden_data_cong <- OnoBurden_data[OnoBurden_data$office == "Congress", ]
  target_dist_marginal <- OnoBurden$target_dist_marginal

  # model-based estimation with regularization
  out_model <-
    model_pAMCE(formula = Y ~ gender + age + family + race + experience + party + pos_security,
                 data = OnoBurden_data_cong,
                 pair_id = OnoBurden_data_cong$pair_id,
                 cluster_id = OnoBurden_data_cong$id,
                 target_dist  = target_dist_marginal, target_type = "marginal")
 summary(out_model, factor_name = c("gender"))

 # decompose the difference in the pAMCEs
 decompose_pAMCE(out_model, effect_name = c("gender", "Female"))