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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sensitivity.R
+\name{sensitivity_perturb}
+\alias{sensitivity_perturb}
+\title{Sensitivity to User-Specified Perturbations}
+\usage{
+sensitivity_perturb(
+  model,
+  exper,
+  perturb,
+  nu_seq = NULL,
+  n_bootstrap = 100,
+  progress = TRUE
+)
+}
+\arguments{
+\item{model}{A \code{multimedia} object containing the fitted models for
+sensitivity analysis. Note that since our approach relies on correlating
+simulated residual error, it is only applicable to models of class
+\code{lm_model()}, \code{glmnet_model()} and \code{rf_model()}.}
+
+\item{exper}{The original \code{mediation_data} class object used to fit \code{model}.
+These observations will be resampled to support bootstrap confidence
+interval construction of the sensitivity curve.}
+
+\item{perturb}{A matrix towards which the original mediator-outcome
+covariance should be perturbed. Must have dimension (n_mediators +
+n_outcomes) x (n_mediators + n_outcomes).}
+
+\item{nu_seq}{The strength of the perturbation towards the matrix perturb.}
+
+\item{n_bootstrap}{The number of bootstrap resamples used to build confidence
+bands around the sensitivity curves. Defaults to 100.}
+
+\item{progress}{A logical indicating whether to show a progress bar.}
+}
+\value{
+A \code{date.frame} giving the outputs of \code{indirect_overall} across many
+values of the correlation rho.
+}
+\description{
+The more standard sensitivity and sensitivity_pathwise functions support
+sensitivity analysis to violations in assumptions restricted to specific
+mediator-outcome pairs. For more general violations, this function allows
+arbitrary modification of the default, diagonal covariance matrix structure
+across both mediators and outcomes. This makes it possible to ask what
+happens when mediators are correlated with one another or when more some
+pairs of mediator-outcome pairs have much stronger correlation than others.
+}
+\details{
+Specifically, it defines a new covariance matrix across mediators and
+outcomes according to diag(sigma^2_mediator, sigma^2_outcome) + nu * perturb.
+The estimates sigma^2 are taken from the residuals in the original mediation
+and outcome models, and perturb and nu are provided by the user.
+}