tost {equivalence} | R Documentation |
This function computes the test and key test quantities for the two one-sided test for equivalence, as documented in Schuirmann (1981) and Westlake (1981). This function computes the test for a sample of paired differences or two samples, assumed to be from a normally-distributed population.
tost(x, y = NULL, alpha = 0.05, epsilon, ...)
x |
the first (or only) sample |
y |
the second sample |
alpha |
test size |
epsilon |
magnitude of region of similarity |
... |
arguments to be passed to t.test |
This test requires the assumption of normality of the population, or an invocation of large-sample theory. The function wraps around the t.test function, so it provides tosts for the same range of designs, accepts the same arguments, and handles missing values the same way.
A list with the following components
mean.diff |
the mean of the difference |
se.diff |
the standard error of the difference |
alpha |
the size of the test |
ci.diff |
the 1-alpha confidence interval for the difference |
df |
the degrees of freedom used for the confidence interval |
epsilon |
the magnitude of the region of similarity |
result |
the outcome of the test of the null hypothesis of dissimilarity |
p.value |
the p-value of the significance test |
check.me |
the confidence interval corresponding to the p-value |
This test requires the assumption of normality of the population. The components of the test are t-based confidence intervals, so the Central Limit Theorem and Slutsky's Theorem may be relevant to its application in large samples.
Andrew Robinson A.Robinson@ms.unimelb.edu.au
Schuirmann, D.L. 1981. On hypothesis testing to determine if the mean of a normal distribution is contained in a known interval. Biometrics 37 617.
Robinson, A.P., and R.E. Froese. 2004. Model validation using equivalence tests. Ecological Modelling 176, 349–358.
Wellek, S. 2003. Testing statistical hypotheses of equivalence. Chapman and Hall/CRC. 284 pp.
Westlake, W.J. 1981. Response to T.B.L. Kirkwood: bioequivalence testing - a need to rethink. Biometrics 37, 589-594.
data(ufc) tost(ufc$Height.m.p, ufc$Height.m, epsilon = 1)