itsadug: Interpreting Time Series and Autocorrelated Data Using GAMMs

Jacolien van Rij, Martijn Wieling, R. Harald Baayen, Dirk van Rijn

Research output: Other contribution › Popular

Original language	English
Type	R package
Media of output	https://cran.r-project.org/package=itsadug
Publication status	Published - 2015

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GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).

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