Inverts the regression-based TOST equivalence test

Share:

Description

This function generates the TOST intervals for the intercept and the slope of the regression of y on x, and determines the smallest region of indifference in each case that would reject the null hypothesis of dissimilarity.

Usage

1
equiv.p(x, y, alpha = 0.05)

Arguments

x

The predictor variable - perhaps the model predictions

y

The response variable - perhaps the observations

alpha

The size of the test

Details

The generated confidence intervals are corrected for experiment-level size of alpha using Bonferroni.

Value

A list of two items:

Intercept

The smallest half-length of the interval that leads to rejection of the null hypothesis of dissimilarity for the intercept, in the units of y.

Slope

The smallest half-length of the interval that leads to rejection of the null hypothesis of dissimilarity for the slope, in the units of the slope.

Note

The accuracy of the output of this function is contingent on the usual regression assumptions, which are not checked here. Caveat emptor!

Author(s)

Andrew Robinson A.Robinson@ms.unimelb.edu.au

References

Robinson, A.P., and R.E. Froese. 2004. Model validation using equivalence tests. Ecological Modelling 176, 349–358.

Robinson, A.P., R.A. Duursma, and J.D. Marshall. 2005. A regression-based equivalence test for model validation: shifting the burden of proof. Tree Physiology 25, 903-913.

See Also

tost.data

Examples

1
2
data(ufc)
equiv.p(ufc$Height.m.p, ufc$Height.m)