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#' Summarize a CIMTx_ATT_sa object
#'
#' @param object a \code{CIMTx_ATT_sa} object obtained with
#' \code{\link{sa}} function.
#' @param ... further arguments passed to or from other methods.
#'
#' @return a data frame containing the estimation, standard error,
#' lower and upper 95\% CI for the causal estimand in terms of RD.
#' @importFrom stringr str_detect str_sub
#' @export
#'
#' @references
#' Hadley Wickham (2019).
#' \emph{stringr: Simple, Consistent Wrappers for Common String Operations}.
#' R package version 1.4.0.
#' URL:\url{https://CRAN.R-project.org/package=stringr}
#'
#' @examples
#' \donttest{
#' lp_w_all <-
#' c(
#' ".4*x1 + .1*x2 - 1.1*x4 + 1.1*x5", # w = 1
#' ".2 * x1 + .2 * x2 - 1.2 * x4 - 1.3 * x5"
#' ) # w = 2
#' nlp_w_all <-
#' c(
#' "-.5*x1*x4 - .1*x2*x5", # w = 1
#' "-.3*x1*x4 + .2*x2*x5"
#' ) # w = 2
#' lp_y_all <- rep(".2*x1 + .3*x2 - .1*x3 - 1.1*x4 - 1.2*x5", 3)
#' nlp_y_all <- rep(".7*x1*x1 - .1*x2*x3", 3)
#' X_all <- c(
#' "rnorm(0, 0.5)", # x1
#' "rbeta(2, .4)", # x2
#' "runif(0, 0.5)", # x3
#' "rweibull(1,2)", # x4
#' "rbinom(1, .4)" # x5
#' )
#' set.seed(1111)
#' data <- data_sim(
#' sample_size = 100,
#' n_trt = 3,
#' x = X_all,
#' lp_y = lp_y_all,
#' nlp_y = nlp_y_all,
#' align = FALSE,
#' lp_w = lp_w_all,
#' nlp_w = nlp_w_all,
#' tau = c(0.5, -0.5, 0.5),
#' delta = c(0.5, 0.5),
#' psi = 2
#' )
#' c_grid <- c(
#' "runif(-0.6, 0)", # c(1,2)
#' "runif(0, 0.6)", # c(2,1)
#' "runif(-0.6, 0)", # c(2,3)
#' "seq(-0.6, 0, by = 0.3)", # c(1,3)
#' "seq(0, 0.6, by = 0.3)", # c(3,1)
#' "runif(0, 0.6)" # c(3,2)
#' )
#' sensitivity_analysis_parallel_ATT_result <-
#' sa(
#' m1 = 1,
#' x = data$covariates,
#' y = data$y,
#' w = data$w,
#' prior_c_function = c_grid,
#' nCores = 1,
#' estimand = "ATE",
#' )
#' summary(sensitivity_analysis_parallel_ATT_result)
#' }
summary.CIMTx_ATT_sa <- function(object, ...) {
object <- object[stringr::str_detect(names(object), "ATT")]
reference_trt <-
as.integer(stringr::str_sub(names(object)[1], 7, 7))
n_trt <-
length(unique(as.integer(stringr::str_sub(
names(object), 8, 8
)))) + 1
w_ind_no_reference <-
unique(as.integer(stringr::str_sub(names(object), 8, 8)))
result_final <- NULL
counter <- 1
for (k in 1:(n_trt - 1)) {
assign(paste0("mean", reference_trt, w_ind_no_reference[k]),
mean(object[[paste0("ATT_RD",
reference_trt, w_ind_no_reference[k])]]))
assign(paste0("sd", reference_trt, w_ind_no_reference[k]),
stats::sd(object[[paste0("ATT_RD",
reference_trt, w_ind_no_reference[k])]]))
assign(
paste0("lower", reference_trt, w_ind_no_reference[k]),
eval(parse(
text = paste0("mean", reference_trt, w_ind_no_reference[k])
)) - 1.96 * eval(parse(
text = paste0("sd", reference_trt, w_ind_no_reference[k])
))
)
assign(
paste0("upper", reference_trt, w_ind_no_reference[k]),
eval(parse(
text = paste0("mean", reference_trt, w_ind_no_reference[k])
)) + 1.96 * eval(parse(
text = paste0("sd", reference_trt, w_ind_no_reference[k])
))
)
assign(paste0("RD", reference_trt, w_ind_no_reference[k]),
round(c(
eval(parse(
text = paste0("mean", reference_trt, w_ind_no_reference[k])
)), eval(parse(
text = paste0("sd", reference_trt, w_ind_no_reference[k])
)), eval(parse(
text = paste0("lower", reference_trt, w_ind_no_reference[k])
)), eval(parse(
text = paste0("upper", reference_trt, w_ind_no_reference[k])
))
), 2))
result_final <-
rbind(result_final, eval(parse(
text = paste0("RD", reference_trt, w_ind_no_reference[k])
)))
rownames(result_final)[[counter]] <-
paste0("ATT_RD", reference_trt, w_ind_no_reference[k])
counter <- counter + 1
}
colnames(result_final) <- c("EST", "SE", "LOWER", "UPPER")
return(result_final)
}
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