R/confint_indirect_list.R

In manymome: Mediation, Moderation and Moderated-Mediation After Model Fitting

#' @title Confidence Intervals of
#' Indirect Effects in an 'indirect_list'
#' Object
#'
#' @description Return the
#' confidence intervals of the indirect
#' effects
#' stored in the output of
#' [many_indirect_effects()].
#'
#' @details It extracts and returns the
#' stored confidence interval
#' if available.
#'
#' The type of confidence intervals
#' depends on the call used to
#' compute the effects. This function
#' merely retrieves the stored estimates,
#' which could be generated by
#' nonparametric bootstrapping,
#' Monte Carlo simulation, or other
#' methods to be supported in
#' the future, and uses them to form the
#' percentile confidence interval.
#'
#' @param object The output of
#' [many_indirect_effects()].
#'
#' @param parm Ignored for now.
#'
#' @param level The level of confidence,
#' default is .95, returning the 95%
#' confidence interval.
#'
#' @param ...  Additional arguments.
#' Ignored by the function.
#'
#' @return A two-column data frame.
#' The columns are the limits of
#' the confidence intervals.
#'
#' @seealso [many_indirect_effects()]
#'
#' @examples
#'
#' library(lavaan)
#' data(data_serial_parallel)
#' mod <-
#' "
#' m11 ~ x + c1 + c2
#' m12 ~ m11 + x + c1 + c2
#' m2 ~ x + c1 + c2
#' y ~ m12 + m2 + m11 + x + c1 + c2
#' "
#' fit <- sem(mod, data_serial_parallel,
#'            fixed.x = FALSE)
#' # All indirect paths from x to y
#' paths <- all_indirect_paths(fit,
#'                            x = "x",
#'                            y = "y")
#' paths
#' # Indirect effect estimates
#' # R should be 2000 or even 5000 in real research
#' # parallel should be used in real research.
#' fit_boot <- do_boot(fit, R = 45, seed = 8974,
#'                     parallel = FALSE,
#'                     progress = FALSE)
#' out <- many_indirect_effects(paths,
#'                              fit = fit,
#'                              boot_ci = TRUE,
#'                              boot_out = fit_boot)
#' out
#' confint(out)
#'
#'
#'
#' @export

confint.indirect_list <- function(object, parm = NULL, level = .95, ...) {
    p <- length(object)
    has_ci <- FALSE
    if (isTRUE(!is.null(object[[1]]$boot_ci))) {
        has_ci <- TRUE
        ci_type <- "boot"
      }
    if (isTRUE(!is.null(object[[1]]$mc_ci))) {
        has_ci <- TRUE
        ci_type <- "mc"
      }
    if (has_ci) {
        confint0 <- lapply(object, stats::confint,
                           parm = parm,
                           level = level, ...)
        confint0 <- do.call(rbind, confint0)
        rownames(confint0) <- names(object)
      } else {
        warning("Confidence intervals not in the object.")
        confint0 <- array(data = rep(NA, p * 2),
                          dim = c(p, 2),
                          dimnames = list(names(object), c("CI.lo", "CI.hi")))
      }
    confint0
  }