Library of functions to design and analyse closed testing procedures for the comparison of population parameters based on independent samples.

Library of functions to design and analyse closed testing procedures for treatment comparisons.
The comparison of the population parameters are based on independent samples. The function `IntersectHypotheses`

creates the hypothesis tree (closure set) of a closed testing procedure. All possible intersecting hypotheses derived from
the list of elementary hypotheses(i.e. the hypotheses to be tested) are produced. The resultant hypothesis tree
will consist of the elementary hypotheses as well as all subsequent intersected hypotheses. The procedure ends when
one hypothesis (the global hypothesis) remains. In this way, for each elementary hypothesis all hypotheses implying
it can be found.

The analysis is performed using `AnalyseCTP`

. First the raw p-values are computed for all hypotheses of the
hypothesis tree, then the p-values of the elementary hypotheses are adjusted according to the closure principle i.e. the adjusted p-value is
calculated as the maximum of the raw p-value of the hypothesis in question and of the raw p-values of all
hypotheses implying it.

Instead of applying `AnalyseCTP`

, the raw p-values can be computed
using any other available software. For this purpose, the functions `Adjust_raw`

is provided.

and
`summary.ctp.str`

generates a data frame comprising all the hypothesis of the hypothesis tree.
The function `Adjust_raw`

calculates the p-values for the given hypothesis tree from the raw p-values
provided in the same order as the hypotheses occur in the data frame created by `summary.ctp.str`

.

The results are presented using the generic functions `summary`

and `Display`

.

J. Bock & P.Jordan

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.