nn: Nearest-neighbors method

In optinterv: Optimal Intervention

Description

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

Usage

nn(Y, X, control = NULL, wgt = rep(1, length(Y)), lambda = 100,
  sigma = 1, grp.size = 30, ...)

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.
grp.size	for the nearest-neighbors method; if the number of examples in each control group is smaller than grp.size, performs weight adjustment using wgt_adjust. else, calculate weights seperatly for each control group.
...	additional arguments.

Value

vector of adjusted weights under I = 1

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