The purpose of this page is just to serve as todo or scratch pad for the development project and to list and share some ideas.

The code development project mentioned on this page has been finished by now. Chances are that this page is considerably outdated and irrelevant. The notes here might not reflect the current state of the code, and you should not use this as serious documentation.

Redesign and implement a common statistical backend for various data types

Objectives

Add support for each major statistical method to each of the data objects (source, freq, timelock)
Clarify the conceptial structure of the parametric and non-parametric statistical tests to the end-user
Ensure that new statistical ideas can easily be implemented and made available to the end-user

Step 1: verify the implementations on simulated data

The purpose is to examine the correctness and completeness of the currently implemented methods. Backward compatibility with exising scripts has to be maintained, therefore the current implementation can serve as reference implementation.

Besides showing that the current implementations are correct, this also reveals the different output formats that are presented to the end user.

Step 2: implement a new structure

The current structure consists of a collection of stand-alone functions which do not fit into a common framework

sourcestatistics_randomization, sourcestatistics_randcluster
clusterrandanalysis, sourcestatistics_parametric, sourcestatistics_shiftpredict, freqstatistics_shiftpredict (*)
timelockstatistics, freqstatistics, sourcestatistics

The timelockstatistics, freqstatistics and sourcestatistics functions provide the user interface to the common statistics structure. Each of these functions calls the general statistics_wrapper, which subsequently dispatches teh computation to a dataformat independent statistics function. The functionality provided by the functions marked by (*) can be merged into the common framework.

The suggested structure is

An alternative to this structure with a more clear separation between the massive univariate single-sample (time-freq-channel element, or voxel) is given below

Implementing a new structure requires that

existing statistical tests have to be refitted under the common structure
handling of critical values has to be implemented consistently (required for parametric tests and clustering)
missing values have to be dealt with (if possible)
multiple comparison corrections have to be implemented consistently, (not only maxStat and clusterSum, but also Bonferoni and FDR)