% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/feature_nca.R
\name{assess_nca_esse}
\alias{assess_nca_esse}
\title{NCA with Effect Size Sensitivity Extension (NCA-ESSE)}
\usage{
assess_nca_esse(
  seminr_model,
  target,
  predictors = NULL,
  thresholds = seq(0, 0.05, by = 0.005),
  ceiling = "ce_fdh",
  test.rep = 0,
  steps = 10,
  seed = 123,
  ...
)
}
\arguments{
\item{seminr_model}{An estimated SEMinR model from \code{estimate_pls()}.}

\item{target}{Name of the endogenous (outcome) construct.}

\item{predictors}{Optional character vector of predictor construct names.
If \code{NULL} (default), auto-detected from the structural model.}

\item{thresholds}{Numeric vector of ECDF thresholds to evaluate
(default \code{seq(0, 0.05, by = 0.005)}). Values must be in [0, 1].}

\item{ceiling}{Ceiling technique (default \code{"ce_fdh"}). The analytical
benchmark is derived for CE-FDH; other techniques will trigger a warning.}

\item{test.rep}{Number of permutation test repetitions at each threshold
(default 0). Set > 0 to obtain p-values, but note this multiplies
computation time by the number of thresholds.}

\item{steps}{Number of steps in the bottleneck table (default 10).}

\item{seed}{Random seed for reproducibility (default 123).}

\item{...}{Additional arguments (currently unused).}
}
\value{
An object of class \code{nca_esse} containing:
  \item{effect_sizes}{Matrix of empirical effect sizes (thresholds x predictors)}
  \item{benchmark}{Matrix of theoretical benchmark effect sizes}
  \item{delta}{Matrix of empirical minus benchmark differences}
  \item{significance}{Matrix of p-values (NULL if test.rep = 0)}
  \item{pls_model}{The original estimated seminr model}
  \item{target}{Name of the target construct}
  \item{predictors}{Character vector of predictor names}
  \item{thresholds}{Numeric vector of ECDF thresholds used}
  \item{ceiling}{Ceiling technique used}
  \item{n_obs}{Number of observations}
}
\description{
\code{assess_nca_esse} applies the NCA-ESSE method (Becker et al., 2026)
to assess how sensitive NCA effect sizes are to extreme response patterns.
It systematically varies the ECDF ceiling threshold and compares empirical
effect size changes against a theoretical benchmark (joint uniform
distribution) to determine whether a necessary condition is robust.
}
\details{
The method proceeds in three steps:
\enumerate{
  \item Run standard NCA (threshold 0\%) to identify potential necessary
    conditions
  \item Apply NCA-ESSE: vary the ECDF threshold from 0\% to a maximum
    (default 5\%), computing CE-FDH effect sizes at each level and
    comparing against the uniform benchmark
  \item (Optional) Select a threshold where the empirical effect size
    meaningfully exceeds the benchmark for further evaluation
}

At each threshold \code{t}, observations whose joint ECDF_NCA value
(P(X <= x, Y >= y)) is at most \code{t} are treated as extreme and
excluded before computing the CE-FDH ceiling. The uniform benchmark
d = t(1 - ln(t)) gives the expected effect size if no necessity exists.
}
\examples{
library(seminr)
library(seminrExtras)

mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3))
)

mobi_sm <- relationships(
  paths(from = c("Image", "Value"), to = "Satisfaction")
)

mobi_pls <- estimate_pls(data = mobi,
                          measurement_model = mobi_mm,
                          structural_model  = mobi_sm)

\donttest{
esse_result <- assess_nca_esse(mobi_pls,
                                target = "Satisfaction",
                                seed = 123)
print(esse_result)
plot(esse_result, type = "sensitivity")
}

}
\references{
Becker, J.-M., Richter, N. F., Ringle, C. M., & Sarstedt, M. (2026).
Must-have, or maybe not? A sensitivity-based extension to necessary
condition analysis. Journal of Business Research, 206, 115920.
}
\seealso{
\code{\link{assess_nca}} for standard NCA analysis
}
