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

1
2
3
4
5
6
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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
## 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

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.