Description Usage Arguments Value References Examples
View source: R/bootstrap_test.R
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,β_1)-m_2(d,β_2)|≥q ε\ vs.\ H_1: \max_{d\in\mathcal{D}} |m_1(d,β_1)-m_2(d,β_2)|< ε,
where
\mathcal{D}
denotes the dose range. See https://doi.org/10.1080/01621459.2017.1281813 for details.
1 2 |
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.
https://doi.org/10.1080/01621459.2017.1281813
1 2 3 4 | data(IBScovars)
male<-IBScovars[1:118,]
female<-IBScovars[119:369,]
bootstrap_test(male,female,"linear","emax",epsilon=0.35,B=300)
|
Loading required package: lattice
Loading required package: DoseFinding
Loading required package: mvtnorm
Loading required package: alabama
Loading required package: numDeriv
$p.value
[1] 0.08666667
$max.abs.difference
[1] 0.178377
$bootstrap.replications
[1] 300
$estimated.model.param.model1
e0 delta
0.39841267 0.04276686
$estimated.model.param.model2
e0 eMax ed50
0.2200357 0.5171142 1.3956642
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