R: Calculation of combination index for binary mixtures

CIcompX {drc} | R Documentation

## Calculation of combination index for binary mixtures

### Description

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

### Usage

    CIcomp(mixProp, modelList, EDvec)

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

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

### Arguments

`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.

### Details

`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.

### Value

`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).

### Author(s)

Christian Ritz and Ismael Rodea-Palomares

### References

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 Also

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

See also the help page for `metals`.

### Examples

    ## 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

[Package drc version 3.0-1 Index]