CIcompX: Calculation of combination index for binary mixtures
In drc: Analysis of Dose-Response Curves

Description Usage Arguments Details Value Author(s) References See Also Examples

For single mixture data combination indices for effective doses as well as effects may be calculated and visualized.

CIcomp(mixProp, modelList, EDvec)

CIcompX(mixProp, modelList, EDvec, EDonly = FALSE)

plotFACI(effList, indAxis = c("ED", "EF"), caRef = TRUE, 
showPoints = FALSE, add = FALSE, ylim, ...)

`mixProp`	a numeric value between 0 and 1 specifying the mixture proportion/ratio for the single mixture considered.
`modelList`	a list contained 3 models fits using `drm` with the model fit for single mixture ratio being the first element, followed by the 2 model fits of the pure substances.
`EDvec`	a vector of numeric values between 0 and 100 (percentages) coresponding to the effect levels of interest.
`EDonly`	a logical value indicating whether or not only combination indices for effective doses should be calculated.
`effList`	a list returned by `CIcompX`.
`indAxis`	a character indicating whether effective doses ("ED") or effects ("EF") should be plotted.
`caRef`	a logical value indicating whether or not a reference line for concentration addition should be drawn.
`showPoints`	A logical value indicating whether or not estimated combination indices should be plotted.
`add`	a logical value specifying if the plot should be added to the existing plot.
`ylim`	a numeric vector of length 2 giving the range for the y axis.
`...`	additional graphical arguments.

CIcomp calculates the classical combination index for effective doses whereas CIcompX calculates the combination index also for effects as proposed by Martin-Betancor et al. (2015); for details and examples using "drc" see the supplementary material of this paper. The function plotFACI may be used to visualize the calculated combination index as a function of the fraction affected.

CIcomp returns a matrix which one row per ED value. Columns contain estimated combination indices, their standard errors and 95% confidence intervals, p-value for testing CI=1, estimated ED values for the mixture data and assuming concentration addition (CA) with corresponding standard errors.

CIcompX returns similar output both for effective doses and effects (as a list of matrices).

Christian Ritz and Ismael Rodea-Palomares

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.

See mixture for simultaneous modelling of several mixture ratios, but only at the ED50 level.

See also the help page for metals.

## Fitting marginal models for the 2 pure substances
acidiq.0 <- drm(rgr ~ dose, data = subset(acidiq, pct == 999 | pct == 0), fct = LL.4())
acidiq.100 <- drm(rgr ~ dose, data = subset(acidiq, pct == 999 | pct == 100), fct = LL.4())

## Fitting model for single mixture with ratio 17:83
acidiq.17 <- drm(rgr ~ dose, data = subset(acidiq, pct == 17 | pct == 0), fct = LL.4())
    
## Calculation of combination indices based on ED10, ED20, ED50
CIcomp(0.17, list(acidiq.17, acidiq.0, acidiq.100), c(10, 20, 50))  
## CI>1 significantly for ED10 and ED20, but not so for ED50