bootstrap_test {TestingSimilarity} | R Documentation |
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.
bootstrap_test(data1, data2, m1, m2, epsilon, B = 2000, bnds1 = NULL,
bnds2 = NULL, plot = FALSE, scal = NULL, off = NULL)
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
data(IBScovars)
male<-IBScovars[1:118,]
female<-IBScovars[119:369,]
bootstrap_test(male,female,"linear","emax",epsilon=0.35,B=300)