Function for testing whether two dose response curves can be assumed as equal concerning the hypotheses

*H_0: \max_{x\in\mathcal{X}} |m_1(d,θ_1)-m_2(d,θ_2)|≥q ε\ vs.\
H_1: \max_{x\in\mathcal{X}} |m_1(d,θ_1)-m_2(d,θ_2)|< ε.*

See http://arxiv.org/pdf/1505.05266.pdf for details.

`data1,data2` |
data frame for each of the two groups containing the variables referenced in dose and resp |

`m1,m2` |
model types. Built-in models are "linlog", "linear", "quadratic", "emax", "exponential", "sigEmax", "betaMod" and "logistic" |

`epsilon` |
positive argument specifying the hypotheses of the test |

`B` |
number of bootstrap replications. If missing, default value of B is 5000 |

`bnds1,bnds2` |
bounds for the non-linear model parameters. If not specified, they will be generated automatically |

`plot` |
if TRUE, a plot of the absolute difference curve of the two estimated models will be given |

`scal,off` |
fixed dose scaling/offset parameter for the Beta/ Linear in log model. If not specified, they are 1.2*max(dose) and 1 respectively |

A list containing the p.value, the maximum absolute difference of the models, the estimated model parameters and the number of bootstrap replications. Furthermore plots of the two models are given.

http://arxiv.org/pdf/1505.05266.pdf

1 2 3 4 5 6 | ```
library("DoseFinding")
library("alabama")
data(IBScovars)
male<-IBScovars[1:118,]
female<-IBScovars[119:369,]
bootstrap_test(male,female,"linear","emax",epsilon=0.35,B=300)
``` |

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