nn_wgt: Nearest-neighbors weights

In optinterv: Optimal Intervention

Description

Calculates unadjusted weights under I = 1, using the nearest-neighbors method

Usage

nn_wgt(Y, X, control = NULL, wgt = rep(1, length(Y)), lambda = 100,
  sigma = 1, test = F)

Arguments

Y	outcome vector (must be numeric without NA's).
X	numeric data frame or matrix of factors to be considered.
control	numeric data frame or matrix of factors to control for. these are factors that we can't consider while looking for the optimal intervention (e.g. race).
wgt	an optional vector of weights.
lambda	the lagrange multiplier. also known as the shadow price of an intervention.
sigma	distance penalty for the nearest-neighbors method.
test	if TRUE, returns weights matrix (only used for testing).

Value

vector of unadjusted weights under I = 1

optinterv documentation built on March 26, 2020, 7:05 p.m.