The function first calculates averaged ERP values within a predetermined number of equally-spaced intervals then tests for significance of the relationship between averaged ERPs and covariates in a linear model framework.
An adaptive factor-adjusted FDR- and FWER-controlling multiple testing procedures for ERP data. The procedure is described in detail in Sheu, Perthame, Lee, and Causeur (2016).
gbtest
(Package: ERP) :
The Guthrie-Buchwald procedure for significance analysis of ERP data
Monte-Carlo implementation of the Guthrie-Buchwald procedure (see Guthrie and Buchwald, 1991) which accounts for the auto-correlation among test statistics to control erroneous detections of short intervals.
The package provides multiple testing procedures designed for Event-Related Potentials (ERP) data in a linear model framework. These procedures are reviewed and compared in Sheu, Perthame, Lee and Causeur (2016). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991), which accounts for the auto-correlation among t-tests to control erroneous detections of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012). It assumes a factor model for the correlation among tests and combines adptatively the estimation of the signal and the updtating of the dependence modelling (see Sheu et al., 2016 for further details).
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