Prepare data for estimation

A series of checks, tests, re-ordering, and other operations to prepare the data for matching. This function can be run standalone, before running multiMatch.

prepareData(Y, W, X, match_on, trimming = NULL, model_options, M_matches,
  J_var_matches)

Arguments

Y	A response vector (1 x n)
W	A treatment vector (1 x n) with numerical values indicating treatment groups
X	A covariate matrix (p x n) with no intercept. When match_on="existing", then X must be a vector (1 x n) of user-specified propensity scores.
match_on	User specifies "covariates" to match on raw covariates, or "existing" to match on user-supplied propensity score values, or "polr" or "multinom" to fit a propensity score model.
trimming	an indicator of whether trimming the sample to ensure overlap
model_options	A list of the options to pass to propensity model. Currently under development. Can only pass reference level to multinomial logistic regression.
M_matches	Number of matches per unit for imputing potential outcomes, as in Abadie and Imbens (2006).
J_var_matches	Number of matches when estimating \(\sigma^2(X,W)\) as in Abadie and Imbens (2006).

Value

A list of information, including the X, W, Y arguments after sorting observeations, and information on unit_ids, etc.

Examples


 sim_data <- multilevelMatching::simulated_data
 Y <- sim_data$outcome
 W <- sim_data$treatment
 X <- as.matrix(sim_data[ ,-(1:2)])
 names(Y) <- paste0("ID", 1:length(Y))

 trimming <- FALSE
 method <- c("covariates", "polr", "multinom")[2]

 prepared_data <- prepareData(
   Y = Y,
   W = W,
   X = X,
   match_on = "polr",
   trimming = FALSE,
   model_options = list(reference_level = sort(W)[1]),
   M_matches = 3,
   J_var_matches = 2
 )

Arguments

Value

Examples

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