Data from heavy metal mixture experiments
Data are from a study of the response of the cyanobacterial self-luminescent metallothionein-based whole-cell biosensor Synechoccocus elongatus PCC 7942 pBG2120 to binary mixtures of 6 heavy metals (Zn, Cu, Cd, Ag, Co and Hg).
data("metals")A data frame with 543 observations on the following 3 variables.
metala factor with levels Ag AgCd Cd Co CoAg CoCd Cu CuAg CuCd CuCo CuHg CuZn Hg HgCd HgCo Zn ZnAg ZnCd ZnCo ZnHg
conca numeric vector of concentrations
BIFa numeric vector of luminescence induction factors
Data are from the study described by Martin-Betancor et al. (2015).
Martin-Betancor, K. and Ritz, C. and Fernandez-Pinas, F. and Leganes, F. and Rodea-Palomares, I. (2015) Defining an additivity framework for mixture research in inducible whole-cell biosensors, Scientific Reports 17200.
## One example from the paper by Martin-Betancor et al (2015)
## Figure 2
## Fitting a model for "Zn"
Zn.lgau <- drm(BIF ~ conc, data = subset(metals, metal == "Zn"),
fct = lgaussian(), bcVal = 0, bcAdd = 10)
## Plotting data and fitted curve
plot(Zn.lgau, log = "", type = "all",
xlab = expression(paste(plain("Zn")^plain("2+"), " ", mu, "", plain("M"))))
## Calculating effective doses
ED(Zn.lgau, 50, interval = "delta")
ED(Zn.lgau, -50, interval = "delta", bound = FALSE)
ED(Zn.lgau, 99.999,interval = "delta") # approx. for ED0
## Fitting a model for "Cu"
Cu.lgau <- drm(BIF ~ conc, data = subset(metals, metal == "Cu"),
fct = lgaussian())
## Fitting a model for the mixture Cu-Zn
CuZn.lgau <- drm(BIF ~ conc, data = subset(metals, metal == "CuZn"),
fct = lgaussian())
## Calculating effects needed for the FA-CI plot
CuZn.effects <- CIcompX(0.015, list(CuZn.lgau, Cu.lgau, Zn.lgau),
c(-5, -10, -20, -30, -40, -50, -60, -70, -80, -90, -99, 99, 90, 80, 70, 60, 50, 40, 30, 20, 10))
## Reproducing the FA-cI plot shown in Figure 5d
plotFACI(CuZn.effects, "ED", ylim = c(0.8, 1.6), showPoints = TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.