R/estimable.R

In LW1949: An Automated Approach to Evaluating Dose-Effect Experiments Following Litchfield and Wilcoxon (1949)

#' Determine if a Dose-Effect Relation is Estimable
#'
#' Determine if a dose-effect relation is estimable based on available data.
#' @param DEdata
#'   A data frame of dose-effect data (typically, the output from
#'     \code{\link{dataprep}}) containing at least two variables: dose,
#'     a numeric vector of chemical concentrations, and pfx,
#'     a numeric vector of proportional effects at each dose (see Details).
#' @return
#'   A logical scalar indicating if a dose-effect relation is estimable.
#' @export
#' @import
#'   stats
#' @details
#'   A dose-effect relation is defined to be estimable if and only if there are
#'     at least two test records and there is some (non-zero) variability in
#'     both the doses and the proportional effects.
#'   The input data are expected to be summarized by dose.
#'     If duplicate doses are provided, an error will be thrown.
#' @examples
#' conc <- c(0.0625, 0.125, 0.25, 0.5, 1)
#' numtested <- rep(8, 5)
#' nalive <- c(1, 4, 4, 7, 8)
#' mydat <- dataprep(dose=conc, ntot=numtested, nfx=nalive)
#' estimable(mydat)
#' nalive2 <- rep(4, 5)
#' mydat2 <- dataprep(dose=conc, ntot=numtested, nfx=nalive2)
#' estimable(mydat2)

estimable <- function(DEdata) {
  if (!is.data.frame(DEdata)) stop("DEdata must be a data frame.")
  if (any(is.na(match(c("dose", "pfx"), names(DEdata))))) {
    stop("DEdata must include at least two variables: dose, pfx.")
  }
  dose.nona <- DEdata$dose[!is.na(DEdata$dose)]
  if (sum(duplicated(dose.nona))>0) {
    stop("Dose should be a vector of unique values, with no duplicates")
  }
  nonmiss <- !is.na(DEdata$dose) & !is.na(DEdata$pfx)
  if (sum(nonmiss) < 1.5) {
    out <- FALSE
  } else {
    out <- !(var(DEdata$dose[nonmiss]) < 0.00000001 |
      var(DEdata$pfx[nonmiss]) < 0.00000001)
  }
  out
  }