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#' Compute Integrated Biomarker Index
#'
#' \code{beliaeff2002} calculates IBR in the hypothetical case study. This is
#' not meant to be called directly.
#'
#' @param sam_mean a data frame, the third output of \code{\link{simul}}.
#'
#' @references Beliaeff, B., & Burgeot, T. (2002). Integrated biomarker
#' response: A useful tool for ecological risk assessment. Environmental
#' Toxicology and Chemistry, 21(6), 1316–1322. \doi{10.1002/etc.5620210629}.
#'
#' @return \code{beliaeff2002} returns a data frame of IBR.
#'
#' @export
beliaeff2002 <- function(sam_mean){
mean.Bmk1 <-
Site <- zscore <- mean.Bmk2 <- mean.Bmk3 <- mean.Bmk4 <-
mean.Bmk5 <-
Bmk1 <-
Bmk2 <-
Bmk3 <-
Bmk4 <- Bmk5 <- NULL #no visible binding for global variable
#Bmk1
pool <- sam_mean %>% summarise(mean = mean(mean.Bmk1),
sd = sd(mean.Bmk1))
temp <- sam_mean %>% select(Site, mean.Bmk1) %>% rowwise %>%
mutate(zscore = (mean.Bmk1 - pool$mean) / pool$sd) #stimu
dat1 <-
temp %>% mutate(trans.score = zscore + abs(min(temp$zscore)))
#Bmk2
pool <- sam_mean %>% summarise(mean = mean(mean.Bmk2),
sd = sd(mean.Bmk2))
temp <- sam_mean %>% select(Site, mean.Bmk2) %>% rowwise %>%
mutate(zscore = -(mean.Bmk2 - pool$mean) / pool$sd) #inhibition
dat2 <-
temp %>% mutate(trans.score = zscore + abs(min(temp$zscore)))
#Bmk3
pool <- sam_mean %>% summarise(mean = mean(mean.Bmk3),
sd = sd(mean.Bmk3))
temp <- sam_mean %>% select(Site, mean.Bmk3) %>% rowwise %>%
mutate(zscore = (mean.Bmk3 - pool$mean) / pool$sd) #stimu
dat3 <-
temp %>% mutate(trans.score = zscore + abs(min(temp$zscore)))
#Bmk4
pool <- sam_mean %>% summarise(mean = mean(mean.Bmk4),
sd = sd(mean.Bmk4))
temp <- sam_mean %>% select(Site, mean.Bmk4) %>% rowwise %>%
mutate(zscore = -(mean.Bmk4 - pool$mean) / pool$sd) #inhibition
dat4 <-
temp %>% mutate(trans.score = zscore + abs(min(temp$zscore)))
#Bmk5
pool <- sam_mean %>% summarise(mean = mean(mean.Bmk5),
sd = sd(mean.Bmk5))
temp <- sam_mean %>% select(Site, mean.Bmk5) %>% rowwise %>%
mutate(zscore = (mean.Bmk5 - pool$mean) / pool$sd) #stimu
dat5 <-
temp %>% mutate(trans.score = zscore + abs(min(temp$zscore)))
#Index
idx <-
data.frame(
Site = dat1$Site,
Bmk1 = dat1$trans.score,
Bmk2 = dat2$trans.score,
Bmk3 = dat3$trans.score,
Bmk4 = dat4$trans.score,
Bmk5 = dat5$trans.score
) %>%
mutate(
Index = 0.5 * Bmk1 * Bmk2 * sin(72 * pi / 180) +
0.5 * Bmk2 * Bmk3 * sin(72 * pi / 180) +
0.5 * Bmk3 * Bmk4 * sin(72 * pi / 180) +
0.5 * Bmk4 * Bmk5 * sin(72 * pi / 180) +
0.5 * Bmk5 * Bmk1 * sin(72 * pi / 180)
)
idx
}
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