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#' @title Plot of Population Attributable Fraction under different values of
#' Relative Risk's parameter theta
#'
#' @description Function that plots the \code{\link{paf}} under different values
#' of a univariate parameter \code{theta} of the Relative Risk function \code{rr}
#' which depends on the exposure \code{X} and a \code{theta} parameter
#' (\code{rr(X, theta)})
#'
#' @param X Random sample (\code{data.frame}) which includes exposure
#' and covariates or sample \code{mean} if \code{"approximate"} method is
#' selected.
#'
#' @param thetalow Minimum of \code{theta} (parameter of relative risk
#' \code{rr}) for plot.
#'
#' @param thetaup Maximum of \code{theta} (parameter of relative risk
#' \code{rr}) for plot.
#'
#' @param rr \code{function} for Relative Risk which uses parameter
#' \code{theta}. The order of the parameters should be \code{rr(X, theta)}.
#'
#' \strong{ **Optional**}
#'
#' @param weights Normalized survey \code{weights} for the sample \code{X}.
#'
#' @param method Either \code{"empirical"} (default), \code{"kernel"} or
#' \code{"approximate"}. For details on estimation methods see
#' \code{\link{pif}}.
#'
#' @param confidence Confidence level \% (default \code{95}).
#'
#' @param confidence_method Either \code{bootstrap} (default) \code{inverse},
#' \code{one2one}, \code{linear}, \code{loglinear}. See \code{\link{paf}}
#' details for additional information.
#'
#' @param Xvar Variance of exposure levels (for \code{"approximate"}
#' method).
#'
#' @param deriv.method.args \code{method.args} for
#' \code{\link[numDeriv]{hessian}} (for \code{"approximate"} method).
#'
#' @param deriv.method \code{method} for \code{\link[numDeriv]{hessian}}.
#' Don't change this unless you know what you are doing (for
#' \code{"approximate"} method).
#'
#' @param ktype \code{kernel} type: \code{"gaussian"},
#' \code{"epanechnikov"}, \code{"rectangular"}, \code{"triangular"},
#' \code{"biweight"}, \code{"cosine"}, \code{"optcosine"} (for \code{"kernel"}
#' method). Additional information on kernels in \code{\link[stats]{density}}.
#'
#' @param bw Smoothing bandwith parameter (for
#' \code{"kernel"} method) from \code{\link[stats]{density}}. Default
#' \code{"SJ"}.
#'
#' @param adjust Adjust bandwith parameter (for \code{"kernel"}
#' method) from \code{\link[stats]{density}}.
#'
#' @param n Number of equally spaced points at which the density (for
#' \code{"kernel"} method) is to be estimated (see
#' \code{\link[stats]{density}}).
#'
#' @param nsim Number of simulations to generate confidence intervals.
#'
#' @param mpoints Number of points in plot.
#'
#' @param colors \code{vector} Colors of plot.
#'
#' @param xlab \code{string} Label of x-axis in plot.
#'
#' @param ylab \code{string} Label of y-axis in plot.
#'
#' @param title \code{string} Title of plot.
#'
#' @param check_integrals \code{boolean} Check that counterfactual \code{cft}
#' and relative risk's \code{rr} expected values are well defined for this
#' scenario.
#'
#' @param check_exposure \code{boolean} Check that exposure \code{X} is
#' positive and numeric.
#'
#' @param check_rr \code{boolean} Check that Relative Risk function
#' \code{rr} equals \code{1} when evaluated at \code{0}.
#'
#' @return paf.plot \code{\link[ggplot2]{ggplot}} object with plot of
#' Population Attributable Fraction as function of \code{theta}.
#'
#' @author Rodrigo Zepeda-Tello \email{rzepeda17@gmail.com}
#' @author Dalia Camacho-GarcĂa-FormentĂ \email{daliaf172@gmail.com}
#'
#' @import ggplot2
#'
#' @examples
#'
#' \dontrun{
#' #Example 1: Exponential Relative Risk empirical method
#' #-----------------------------------------------------
#' set.seed(18427)
#' X <- data.frame(Exposure = rbeta(25, 4.2, 10))
#' paf.plot(X, thetalow = 0, thetaup = 2, function(X, theta){exp(theta*X)})
#'
#'
#' #Same example with kernel method
#' paf.plot(X, 0, 2, function(X, theta){exp(theta*X)}, method = "kernel",
#' title = "Kernel method example")
#'
#' #Same example for approximate method
#' Xmean <- data.frame(mean(X[,"Exposure"]))
#' Xvar <- var(X)
#' paf.plot(Xmean, 0, 2, function(X, theta){exp(theta*X)},
#' method = "approximate", Xvar = Xvar, title = "Approximate method example")
#' }
#'
#' @seealso
#'
#' See \code{\link{paf}} for Population Attributable Fraction estimation
#' with confidence intervals \code{\link{paf.confidence}}.
#'
#' See \code{\link{pif.plot}} for same plot with Potential Impact Fraction
#' \code{\link{pif}}.
#'
#' @export
paf.plot <- function(X, thetalow, thetaup, rr,
method = "empirical",
confidence_method = "bootstrap",
confidence = 95,
nsim = 100,
weights = rep(1/nrow(as.matrix(X)),nrow(as.matrix(X))),
mpoints = 100,
adjust = 1, n = 512,
Xvar = var(X),
deriv.method.args = list(),
deriv.method = "Richardson",
ktype = "gaussian",
bw = "SJ",
colors = c("deepskyblue", "gray25"), xlab = "Theta",
ylab = "PAF",
title = "Population Attributable Fraction (PAF) under different values of theta",
check_exposure = TRUE, check_rr = TRUE, check_integrals = TRUE){
pif.plot(X = X, thetalow = thetalow, thetaup = thetaup, rr = rr,
cft=NA, method = method, confidence_method = confidence_method,
confidence = confidence, nsim = nsim, weights = weights, mpoints = mpoints,
adjust = adjust, n = n, Xvar = Xvar,
deriv.method.args = deriv.method.args, deriv.method = deriv.method,
ktype = ktype, bw = bw, colors = colors, xlab = xlab, ylab = ylab,
title = title, check_exposure = check_exposure, check_rr = check_rr,
check_integrals = check_integrals, is_paf = TRUE)
}
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