bootstrap_test: Bootstrap test for the equivalence of dose response curves via the maximum absolute deviation
Description
Function for testing whether two dose response curves can be assumed as equal concerning the
hypotheses $$H_0: \max_{d\in\mathcal{D}} |m_1(d,\beta_1)-m_2(d,\beta_2)|\geq \epsilon\ vs.\
H_1: \max_{d\in\mathcal{D}} |m_1(d,\beta_1)-m_2(d,\beta_2)|< \epsilon,$$
where $$\mathcal{D}$$ denotes the dose range.
See https://doi.org/10.1080/01621459.2017.1281813 for details.
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.