# R/check_integrals.R In pifpaf: Potential Impact Fraction and Population Attributable Fraction for Cross-Sectional Data

#### Documented in check.integrals

```#' @title Check Integrals
#'
#' @description Function for checking that the integrals of \code{\link{pif}} are nonnegative.
#'
#' @param meancft Mean of relative risk \code{rr} with counterfactual
#'
#' @param meanrr  Mean of relative risk \code{rr} without counterfactual
#'
#' @return bool   \code{TRUE} if as desired
#'
#' @examples
#' check.integrals(1,0)
#'
#' \dontrun{
#' check.integrals(0,1)
#' check.integrals(1,-1)
#' }
#'
#' @author Rodrigo Zepeda-Tello \email{[email protected]}
#' @author Dalia Camacho-García-Formentí \email{[email protected]}
#'
#' @seealso \code{\link{check.confidence}}, \code{\link{check.thetas}},
#'   \code{\link{check.cft}}, \code{\link{check.xvar}},
#'   \code{\link{check.rr}}, \code{\link{check.exposure}}
#'
#' @keywords internal
#'
#' @export

check.integrals <- function(meanrr, meancft){

#Boolean variable = 1
.bool <- TRUE

#Check that the relative risk has returned positive > 0 values
if (meanrr <= 0){
.bool <- FALSE
warning("Expected value of relative risk is <= 0!")
}

#Check that the relative risk has returned positive > 0 values
if (meancft < 0){
.bool <- FALSE
warning("Expected value of relative risk under counterfactual is < 0!")
}

#Check that counterfactual is reducing the risk on average
if (meanrr < meancft){
.bool <- FALSE
warning(paste0("Counterfactual is increasing the Risk. Are you sure you",
" are specifying it correctly?"))
}

return(.bool)

}
```

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pifpaf documentation built on Sept. 29, 2017, 1:03 a.m.