# 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 |

### 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 |

`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 |

`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
``` |