model_pAMCE: Estimating the population AMCE using a model-based approach

In naoki-egami/factorEx: Design and Analysis for Factorial Experiments

Estimating the population AMCE using a model-based approach

Description

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.

Usage

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
)

Arguments

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.

Value

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.

References

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.

Examples

  # 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"))

naoki-egami/factorEx documentation built on Oct. 8, 2022, 5:41 a.m.