Lhat_fun_RD: Estimator for the Adaptive CI

In koohyun-kwon/rdadapt:

Estimator for the Adaptive CI

Description

Calculates Lhat_j(δ) in the paper

Usage

Lhat_fun_RD(
  delta,
  Cj,
  Cbar,
  Xt,
  Xc,
  mon_ind,
  sigma_t,
  sigma_c,
  Yt,
  Yc,
  ht,
  hc,
  ret.w = FALSE
)

Arguments

delta	a nonegative scalar value: it can be left unspecified if ht and hc are specified.
Cj	the smoothness parameter aiming to adapt to.
Cbar	the largest smoothness parameter.
Xt	n_t by k design matrix for the treated units.
Xc	n_c by k design matrix for the control units.
mon_ind	index number for monotone variables.
sigma_t	standard deviation of the error term for the treated units (either length 1 or n_t).
sigma_c	standard deviation of the error term for the control units (either length 1 or n_c).
Yt	outcome value for the treated group observations.
Yc	outcome value for the control group observations.
ht	the modulus value for the treated observations; it can be left unspecified if delta is specified.
hc	the modulus value for the control observations; it can be left unspecified if delta is specified.
ret.w	returns weights vector if TRUE.

Value

a scalar value of the estimator

Examples

n <- 500
d <- 2
X <- matrix(rnorm(n * d), nrow = n, ncol = d)
tind <- X[, 1] < 0 & X[, 2] < 0
Xt <- X[tind == 1, ,drop = FALSE]
Xc <- X[tind == 0, ,drop = FALSE]
mon_ind <- c(1, 2)
sigma <- rnorm(n)^2 + 1
sigma_t <- sigma[tind == 1]
sigma_c <- sigma[tind == 0]
Yt = 1 + rnorm(length(sigma_t), mean = 0, sd = sigma_t)
Yc = rnorm(length(sigma_c), mean = 0, sd = sigma_c)
Lhat_fun_RD (1, 1/2, 1, Xt, Xc, mon_ind, sigma_t, sigma_c, Yt, Yc)
Lhat_fun_RD (1, 1/2, Inf, Xt, Xc, mon_ind, sigma_t, sigma_c, Yt, Yc)

koohyun-kwon/rdadapt documentation built on May 8, 2022, 8:49 p.m.