Description Usage Arguments Details Value See Also Examples

According to the fitted concentration-response information of mixtures. The concentration (e.g., ECx) that causes certain effect in the mixture will be calculated. ecaPred will predict how much effect will be caused at ECx according to concentration addition. The individual concentration-responses should be fitted only based on the following six models: Hill, Weibull, Logit, BCW, BCL, and GL.

1 | ```
ecaPred(effv, sgl, mix, pctMix)
``` |

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

`sgl` |
A list with sgl$model and sgl$param. sgl$model is character vector of equations used to fit the concentration-response data of individual chemicals: Hill, Weibull, Logit, BCW, BCL, GL. sgl$param is numeric matrix of fitting coefficients with rownames (equations) and colnames (Alpha, Beta, and Gamma). For equations with only two parameters, Gamma can be set as zero or any other numeric value. |

`mix` |
A list with mix$model and mix$param. mix$model is character vector of equations used to fit the concentration-response data of mixtures: Hill, Weibull, Logit, BCW, BCL, GL. mix$param is numeric matrix of fitting coefficients of mixtures' concentration-response data with rownames (selected equations) and colnames (Alpha, Beta, and Gamma). For equations with only two parameters, Gamma can be set as zero or any other numeric value. |

`pctMix` |
A numeric matrix, the concentration ratio (percent) of every component in the mixture. |

ecaPred calculate the effect concentrations based on the fitted concentration-response
information of the mixture according to the input effects effv (e.g., 0.05 and 0.5). The
concentration of individual component *c_i* is computed based on mixture's
ECx and the proportion of component in the mixture *p_i*.
Then the CA effect will be calculated based
on the concentration addition.

A numeric matrix of predicted effects

1 2 3 4 5 6 7 8 9 | ```
## example
# predict the CA predicted response at the concentrations that cause 5%, 10%, 20%, and 50%
# effect of antibiotic mixtures
# each mixture contains eight components. Totally, there are 10 mixtures designed by the udcr.
sgl <- antibiotox$sgl
mix <- antibiotox$udcr.mix
pct <- antibiotox$udcr.pct
ecaPred(effv = c(0.05, 0.1, 0.20, 0.5), sgl, mix, pct)
``` |

```
u1 u2 u3 u4 u5 u6
caPred-E5 0.03921190 0.06546702 0.03178998 0.02942534 0.04759492 0.03619126
caPred-E10 0.08035662 0.10816020 0.06589418 0.05890705 0.09230186 0.07559683
caPred-E20 0.16620828 0.18295145 0.13917512 0.12608569 0.18096058 0.15968645
caPred-E50 0.44075816 0.40753116 0.39207710 0.39288565 0.45098713 0.43413717
u7 u8 u9 u10
caPred-E5 0.02370444 0.05999021 0.02263654 0.03435294
caPred-E10 0.05275136 0.09817532 0.05031045 0.07180578
caPred-E20 0.12092925 0.16568196 0.11631439 0.15334770
caPred-E50 0.38567732 0.37637132 0.37995642 0.43245141
```

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