# 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{rzepeda17@gmail.com}
#' @author Dalia Camacho-GarcĂ­a-FormentĂ­ \email{daliaf172@gmail.com}
#'
#'
#' @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)

}
```

## Try the pifpaf package in your browser

Any scripts or data that you put into this service are public.

pifpaf documentation built on May 1, 2019, 9:11 p.m.