CI_adpt: Adaptive Confidence Interval
In koohyun-kwon/rdadapt:

CI_adpt

R Documentation

Adaptive Confidence Interval

Description

Calculates the one-sided or two-sided adaptive confidence interval

Usage

CI_adpt(
  Cvec,
  Cbar,
  Xt,
  Xc,
  mon_ind,
  sigma_t,
  sigma_c,
  Yt,
  Yc,
  alpha,
  lower = TRUE,
  num_sim = 10^5,
  delta_init = 1.96,
  bmat_init_L,
  bmat_L,
  tau_res
)

Arguments

`Cvec`	a sequence of smoothness parameters
`Cbar`	the Lipschitz coefficient for the largest function space we consider
`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.
`alpha`	desired upper quantile value.
`lower`	calculate a lower one-sided confidence interval if `TRUE`; calculate a two-sided CI otherwise.
`num_sim`	number of simulations used to calculate the quantile; the default is `10^5`.
`delta_init`	the value of δ to be used in simulating the quantile; theoretically, its value does not matter asymptotically. Its default value is 1.96.
`bmat_init_L`	the matrix of modulus values corresponding to `delta_init` and `Cvec`; it can be left unspecified.
`bmat_L`	the matrix of modulus values corresponding to the optimal δ and `Cvec`; it can be left unspecified.
`tau_res`	outcome value from the function `tau_res`; it can be left unspecified.

Details

This returns either one-sided lower CI or two-sided Bonferroni CI using the function CI_adpt_L; it seems this function will be substituted by CI_adpt_opt defined below.

Value

confidence interval endpoints.

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)
CI_adpt((1:5)/5, 1, Xt, Xc, mon_ind, sigma_t, sigma_c, Yt, Yc, 0.05, lower = FALSE)
CI_adpt((1:5)/5, 1, Xt, Xc, mon_ind, sigma_t, sigma_c, Yt, Yc, 0.05, lower = TRUE)
CI_adpt((1:5)/5, Inf, Xt, Xc, mon_ind, sigma_t, sigma_c, Yt, Yc, 0.05, lower = TRUE)

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