Interpreting Time Series and Autocorrelated Data Using GAMMs
Generate N ACF plots of individual or aggregated time series.
Generate an ACF plot of an aggregated time series.
Generate an ACF plot of model residuals. Works for lm, lmer, gam, bam,...
Inspect residuals of regression models.
Function for comparing two GAMM models.
Prepare string for regular expressions (backslash for all non-letter a...
Calculate the correlation between the fitted model and data.
Derive the time series used in the AR1 model.
Visualization of the model fit for time series data.
Compare the formulas of two models and return the difference(s).
Calculate the dispersion of the residuals
Fade out the areas in a surface without data.
Return the regions in which the smooth is significantly different from...
Visualization of nonlinear interactions, summed effects.
Convert model summary into Latex/HTML table for knitr/R Markdown repor...
Get coefficients for the parametric terms (intercepts and random slope...
Get model predictions for differences between conditions.
Get model all fitted values.
Get estimated for selected model terms.
Return PCA predictions.
Get model predictions for specific conditions.
Get coefficients for the random intercepts and random slopes.
Information on how to cite this package
Turn on or off information messages.
Inspection and interpretation of random factor smooths.
Interpreting Time Series, Autocorrelated Data Using GAMMs (itsadug)
Return indices of data that were not fitted by the model.
Retrieve the degrees of freedom specified in the model.
Number of observations in the model.
Visualization of the model fit for time series data.
Plot difference curve based on model predictions.
Plot difference surface based on model predictions.
Visualization of the model fit for time series data.
Visualization of group estimates.
Visualization of the effect predictors in nonlinear interactions with ...
Visualization of smooths.
Visualization of EEG topo maps.
Print a named list of strings, output from summary_data.
Visualization of partial nonlinear interactions.
Return a list with reference levels for each factor.
Returns a description of the statistics of the smooth terms for report...
Retrieve the residual degrees of freedom from the model.
Extract model residuals and remove the autocorrelation accounted for.
Add rug to plot, based on model.
Determine the starting point for each time series.
Extract the Lag 1 value from the ACF of the residuals of a gam, bam, l...
Print a descriptive summary of a data frame.
Label timestamps as timebins of a given binsize.
Function for post-hoc comparison of the contrasts in a single GAMM mod...
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).