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#' Compute Rank Sum Biomarker Index
#'
#' \code{blaise2002} calculates RSI in the hypothetical case study. This is not
#' meant to be called directly.
#'
#' @param sam a data frame, the first output of \code{\link{simul}}.
#' @param sam_mean a data frame, the third output of \code{\link{simul}}.
#'
#' @references Blaise, C., Gagné, F., Pellerin, J., Hansen, P.-D., & Trottier,
#' S. (2002). Molluscan shellfish biomarker study of the Quebec, Canada,
#' Saguenay Fjord with the soft-shell clam, Mya arenaria. Environmental
#' Toxicology, 17(3), 170–186. \doi{10.1002/tox.10048}.
#'
#' @return \code{blaise2002} returns a data frame of RSI.
#'
#' @export
blaise2002 <- function(sam, sam_mean){
Bmk1 <- Bmk2 <- Bmk3 <- Bmk4 <- Bmk5 <- NULL #no visible binding for global variable
#Bmk1
sorting <- sam_mean$Site[order(sam_mean$mean.Bmk1)] #stimu
rank <- rep(1, 5)
for (i in 1:4) {
test.result <- wilcox.test(sam$Bmk1[sam$Site == sorting[i]],
sam$Bmk1[sam$Site == sorting[i + 1]])
if (test.result$p.value <= 0.05)
rank[i + 1] <- rank[i] + 1
else
rank[i + 1] <- rank[i]
}
df1 <- data.frame(sorting, Bmk1 = rank)
#Bmk2
sorting <- sam_mean$Site[order(-sam_mean$mean.Bmk2)] #inhibit
rank <- rep(1, 5)
for (i in 1:4) {
test.result <- wilcox.test(sam$Bmk2[sam$Site == sorting[i]],
sam$Bmk2[sam$Site == sorting[i + 1]])
if (test.result$p.value <= 0.05)
rank[i + 1] <- rank[i] + 1
else
rank[i + 1] <- rank[i]
}
df2 <- data.frame(sorting, Bmk2 = rank)
#Bmk3
sorting <- sam_mean$Site[order(sam_mean$mean.Bmk3)] #stimu
rank <- rep(1, 5)
for (i in 1:4) {
test.result <- wilcox.test(sam$Bmk3[sam$Site == sorting[i]],
sam$Bmk3[sam$Site == sorting[i + 1]])
if (test.result$p.value <= 0.05)
rank[i + 1] <- rank[i] + 1
else
rank[i + 1] <- rank[i]
}
df3 <- data.frame(sorting, Bmk3 = rank)
#Bmk4
sorting <- sam_mean$Site[order(-sam_mean$mean.Bmk4)] #inhibit
rank <- rep(1, 5)
for (i in 1:4) {
test.result <- wilcox.test(sam$Bmk4[sam$Site == sorting[i]],
sam$Bmk4[sam$Site == sorting[i + 1]])
if (test.result$p.value <= 0.05)
rank[i + 1] <- rank[i] + 1
else
rank[i + 1] <- rank[i]
}
df4 <- data.frame(sorting, Bmk4 = rank)
#Bmk5
sorting <- sam_mean$Site[order(sam_mean$mean.Bmk5)] #stimu
rank <- rep(1, 5)
for (i in 1:4) {
test.result <- wilcox.test(sam$Bmk5[sam$Site == sorting[i]],
sam$Bmk5[sam$Site == sorting[i + 1]])
if (test.result$p.value <= 0.05)
rank[i + 1] <- rank[i] + 1
else
rank[i + 1] <- rank[i]
}
df5 <- data.frame(sorting, Bmk5 = rank)
#Index
idx <-
list(df1, df2, df3, df4, df5) %>% reduce(full_join, by="sorting") %>%
rowwise %>% mutate(Index = sum(Bmk1, Bmk2, Bmk3, Bmk4, Bmk5)) %>% rename(Site = sorting)
idx
}
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