R/ghost_par_ests.R

In phantSEM: Create Phantom Variables in Structural Equation Models for Sensitivity Analyses

#' Provide parameter estimates from sensitivity analysis function
#'
#' `ghost_par_ests()` Selects certain parameter estimates from the output of the sensitivity analysis.
#' @param step3 The object returned from SA_step3.
#' @param parameter_label The label used for the parameter in the lavaan code.
#' @param remove_NA Remove rows for combinations of phantom variable parameters that resulted in inadmissable solutions in lavaan.
#' @returns A dataframe of the parameter estimates from the lavaan model.
#' @importFrom stats na.omit
#' @export
#'
#' @examples
#' # example code
#'
#' covmatrix <- matrix(c(
#'   0.25, 0.95, 0.43,
#'   0.95, 8.87, 2.66,
#'   0.43, 2.66, 10.86
#' ), nrow = 3, byrow = TRUE)
#' colnames(covmatrix) <- c("X", "M2", "Y2")
#'
#' # lavann syntax for observed model
#' observed <- " M2 ~ X
#'              Y2 ~ M2+X "
#'
#' # lavaan output
#' obs_output <- lavaan::sem(model = observed, sample.cov = covmatrix, sample.nobs = 200)
#'
#' # lavaan syntax for phantom variable model
#' phantom <- " M2 ~ M1 + Y1 + a*X
#'                Y2 ~ M1 + Y1 + b*M2 + cp*X "
#'
#' Step1 <- SA_step1(
#'   lavoutput = obs_output,
#'   mod_obs = observed,
#'   mod_phant = phantom
#' )
#'
#' phantom_assignment <- list(
#'   "CovM1X" = 0,
#'   "CovY1M1" = "CovY2M2",
#'   "CovY1X" = 0,
#'   "VarM1" = 1,
#'   "VarY1" = 1,
#'   "CovM1M2" = seq(.4, .6, .1),
#'   "CovY1Y2" = "CovM1M2",
#'   "CovY1M2" = seq(.2, .4, .1),
#'   "CovM1Y2" = "CovY1M2"
#' )
#' Step2 <- SA_step2(
#'   phantom_assignment = phantom_assignment,
#'   step1 = Step1
#' )
#' Step3 <- SA_step3(
#'   step2 = Step2,
#'   n = 200
#' )
#'
#' b_results <- ghost_par_ests(
#'   step3 = Step3,
#'   parameter_label = "b",
#'   remove_NA = TRUE
#' )
ghost_par_ests <- function(step3,
                           parameter_label,
                           remove_NA = FALSE) {
  resultlist <- step3[[1]]
  combos <- step3[[4]]
  parmatrix <- matrix(NA, nrow = 0, ncol = 10)
  comboindex <- matrix(NA, nrow = 0, ncol = 1)
  colnames(parmatrix) <- c(
    "lhs",
    "op",
    "rhs",
    "label",
    "est",
    "se",
    "z",
    "pvalue",
    "ci.lower",
    "ci.upper"
  )

  for (i in 1:nrow(combos)) {
    reptemp <- i
    if (length(resultlist[[i]]) > 1) {
      pars <- resultlist[[i]]
      partemp <- pars[which(pars$"label" == parameter_label), ]
    } else {
      partemp <- rep(NA, 10)
    }
    parmatrix <- rbind(parmatrix, partemp)
    comboindex <- rbind(comboindex, reptemp)
  }

  out <- cbind(comboindex, parmatrix)
  colnames(out) <-
    c(
      "rep",
      "lhs",
      "op",
      "rhs",
      "label",
      "est",
      "se",
      "z",
      "pvalue",
      "ci.lower",
      "ci.upper"
    )
  # out <- cbind(combos, out)

  if (remove_NA == TRUE) {
    out <- stats::na.omit(out)
  }

  return(out)
}