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# STH SHOULD THIS USE OPTIMAL TREATMENT OR OBSERVED?
#' Estimate the confidence intervals
#'
#' @noRd
#' @param obj A list object. Must contain `$K`, `$models`, `$data`, `$covmat`,
#' `$psi`, `$tx.type`
#'
#' @return A numeric vector object of length `obj$K`
#'
#' @keywords internal
.varest <- function(obj) {
if (is.null(obj$covmat)) return(NA)
if (obj$tx.type != "bin") return(NA)
.each_stage <- function(models, data, covmat, psi) {
H_psi <- stats::model.matrix(models$blip, data)
H_psi <- H_psi[stats::complete.cases(H_psi), ]
# lower/upper limits on psi
tmp <- covmat |> diag() |> sqrt()
psi_l <- psi - 1.96 * tmp
psi_u <- psi + 1.96 * tmp
# look at max/min value of blip based on sign of covariates and max/min of
# parameter CIs
H_psi_p <- pmax(H_psi, 0.0)
H_psi_n <- pmin(H_psi, 0.0)
# lower blip is positive values * lower CI + negative values * upper CI
blip_l <- H_psi_p %*% psi_l + H_psi_n %*% psi_u
blip_u <- H_psi_p %*% psi_u + H_psi_n %*% psi_l
sum(blip_l < 0 & blip_u > 0) / nrow(H_psi)
}
res <- numeric(obj$K)
for (j in obj$K:1L) {
res[j] <- .each_stage(obj$models[[j]], obj$data, obj$covmat[[j]], obj$psi[[j]])
}
res
}
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