CIcompX.RdFor 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 |
| 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. |
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).
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.
## 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))#> combInd SE lowCI highCI CAdiffp ED.CA SE.CA #> 10 1.7180152 0.31407144 1.1024352 2.333595 0.02224534 76.91373 11.85583 #> 20 1.3421604 0.16702874 1.0147841 1.669537 0.04050985 140.38385 14.47436 #> 50 0.9035949 0.08440138 0.7381682 1.069022 0.25336168 382.44378 32.11935 #> ED.mix SE.mix #> 10 132.1390 12.98677 #> 20 188.4176 13.13050 #> 50 345.5742 14.12771## CI>1 significantly for ED10 and ED20, but not so for ED50