caPred | R Documentation |

Predicting mixture toxicity based on individual concentration-response information fitted only based on the following six models: Hill, Weibull, Logit, BCW, BCL, and GL. Three optional mixture design methods are provided:(1)arbitrary concentration ratio (acr), users can set an arbitary proportion for each component in a mixture; (2)equal effect concentration ratio (eecr); (3) uniform design concentration ratio (udcr).

caPred(model, param, mixType = "eecr", effv, effPoints, sav = FALSE)

`model` |
vector of models: Hill, Weibull, Logit, BCW, BCL, and GL |

`param` |
numeric matrix of fitting coefficients with row names (model selected) and column names (Alpha, Beta, and Gamma). For models with only two parameters (i.e., Hill, Weibull, and Logit), Gamma can be set to zero or any other numeric value. |

`mixType` |
experimental design of the mixture. acr: arbitrary concentration ratio; eecr: equal effect concentration ratio; udcr: uniform design concentration ratio. |

`effv` |
numeric vector with single or multiple effects (0 ~ 1). |

`effPoints` |
numeric vector [0 ~ 1] of effects to predict effect concentrations. |

`sav` |
TRUE: save output to a default file; FALSE: output will not be saved; a custom file directory: save output to the custom file directory. |

Concentration addition (CA) is designed for mixtures of chemicals that have similar mechanisms of action. For a well-defined mixture (e.g., a mixture of n components), CA is expressed mathematically as:

*∑\limits_{i = 1}^n {\frac{{{c_i}}}{{EC{x_i}}}} = 1*

where *EC{x_i}* is the effect concentration of the *i^{th}* compound that
causes x% effect when applied individually at *{c_i}*. The *{c_i}* can be
computed from the following equation:

*{c_i} = {p_i} \cdot {c_{mix}} = {p_i} \cdot E{C_{x,mix}}*

where *p_i* is the proportion of *i^{th}* component in the mixture,
*c_{mix}* the mixture concentration and *E{C_{x,mix}}* the
concentration of the mixture at a specific effect x%. The prediction
of combined effects of mixture-components based on
CA can then be expressed as:

*E{C_{x,mix}} = {≤ft( {∑\limits_{i = 1}^n {\frac{{{p_i}}}{{E{C_{x,i}}}}}}
\right)^{ - 1}}*

`ca` |
a series of effect concentrations predicted by CA at effPoints |

`e` |
a series of effects (effPoints) associated with the effect concentrations in ca |

`pct` |
the proportion of every component in a mixture |

`uniTab` |
the uniform design table used to construct the mixture when mixType is 'udcr' |

Note that effv is dependent on the mixType.

If the mixType is acr, effv is supposed to be a vector with the ratio of each components and its length
should be the same as the number of components. For instance, effv can be c(4, 1) if one wants to mix two
compounds with a ratio of 4:1. One can also set effv as c(80, 20) or c(0.8, 0.2) or any other values
with the same ratio as 4:1. The program will convert the ratio to a range of 0 to 100%.

If the mixType is eecr, effv is supposed to be the effect values. For instance, effv = c(0.5) means one mixture
will be prepared and each of its components will be mixed in a ratio equal to EC50. More than one eecr mixtures
can be achieved by assigning more effect values to effv, e.g., effv = c(0.05, 0.10, 0.5)

If the mixType is udcr, elements in effv are levels in a uniform table, the length of effv is the same as
the number of runs. the number of runs should be consistent with the number of components in the uniform design.

Liang, Yi-zeng, Kai-tai Fang, and Qing-song Xu. 2001. Uniform Design and Its Applications in
Chemistry and Chemical Engineering. Chemometrics and Intelligent Laboratory Systems
58(1):43-57.

Backhaus, T., Faust, M., 2012. Predictive environmental risk assessment of chemical
mixtures: A conceptual framework. Environmental Science and Technology. 46, 2564-2573.

`iaPred`

## example 1 # using CA to predict the toxicity of mixture designed by eecr at the # effect concentration of EC05 and EC50 # eecr mixture design is based on seven antibiotics(factors). model <- antibiotox$sgl$model param <- antibiotox$sgl$param caPred(model, param, mixType = "eecr", effv = c(0.05, 0.5)) ## example 2 # using CA to predict the mixtures designed by udcr # the udcr mixture design is based on four heavy metals and four ionic liquids (eight factors). # five levels (EC05, EC10, EC20, EC30, and EC50 ) are allocated in the uniform table using the # pseudo-level technique (Liang et al., 2001) model <- cytotox$sgl$model param <- cytotox$sgl$param effv <- c(0.05, 0.05, 0.10, 0.10, 0.20, 0.20, 0.30, 0.30, 0.50, 0.50) caPred(model, param, mixType = "udcr", effv) ## example 3 # using CA to predict the mixtures designed by acr # the udcr mixture design is based on five antibiotics (five factors). # the every component in the mixture shares exactly the same ratio (0.20) model <- antibiotox$sgl$model[1 : 5] param <- antibiotox$sgl$param[1 : 5, ] effv <- c(0.2, 0.2, 0.2, 0.2, 0.2) caPred(model, param, mixType = "acr", effv)

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