FLAME (version 1.0.0)

FLAME_bit: Bit Vectors Implementation

Description

FLAME_bit applies FLAME matching algorithm based on bit vectors. The required arguments include (1) data and (2) holdout. The rest of the arguments are optional.

Usage

FLAME_bit(data, holdout, tradeoff = 0.1, compute_var = FALSE,
  PE_function = NULL, model = NULL, ridge_reg = NULL,
  lasso_reg = NULL, tree_depth = NULL)

Arguments

data

input data

holdout

holdout training data

tradeoff

tradeoff parameter to compute Match Quality (optional, default = 0.1)

compute_var

indicator variable of computing variance (optional, default = FALSE)

PE_function

user defined function to compute predictive error (optional)

model

user defined model - Linear, Ridge, Lasso, or DecisionTree (optional)

ridge_reg

L2 regularization parameter if model = Ridge (optional)

lasso_reg

L1 regularization parameter if model = Lasso (optional)

tree_depth

maximum depth of decision tree if model = DecisionTree (optional)

Value

(1) list of covariates FLAME performs matching at each iteration, (2) Sizes, conditional average treatment effects (CATEs), and variance (if compute_var = TRUE) of matches at each iteration, (3) match quality at each iteration, and (4) the original data with additional column *matched*, indicating the number of covariates each unit is matched on. If a unit is never matched, then *matched* will be 0.

Examples

# NOT RUN {
data(toy_data)
FLAME_bit(data = toy_data, holdout = toy_data)
# }