# bootstrap_test: Bootstrap test for testing dose response curves for... In TestingSimilarity: Bootstrap Test for Similarity of Dose Response Curves Concerning the Maximum Absolute Deviation

### Description

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.

### Arguments

 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

### Value

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.

### Examples

 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) 

TestingSimilarity documentation built on May 20, 2017, 3:12 a.m.

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