This function computes the test and key test quantities for the paired t-test for equivalence, as documented in Wellek (2003, pp 77-80). This function computes the test from a sample of a normally-distributed population.
ptte.data(x, alpha = 0.05, Epsilon = 0.25)A list with the following components
the outcome of the test of the null hypothesis of dissimilarity
the mean of the sample
the standard deviation of the sample
the sample size
the size of the test
the number of observations missing
the magnitude of the region of similarity
the critical value
the test statistic; if Tstat < cutoff then the null hypothesis is rejected.
the power of the test evaluated at the observed value
paired differences
test size
magnitude of region of similarity
Andrew RobinsonA.Robinson@ms.unimelb.edu.au
This test requires the assumption of normality of the population. Under that assumption the test is the uniformly most powerful invariant test (Wellek, 2003, pp. 78-79).
The function as documented by Wellek (2003) uses units relative to the standard deviation, noting (p. 12) that 0.25 corresponds to a strict test and 0.5 to a liberal test.
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.
ptte.stat, tost.data
data(ufc)
ptte.data(ufc$Height.m.p - ufc$Height.m)
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