Cumulative History Analysis for Bistable Perception Time Series
Computes R-squared using Bayesian R-squared approach.
Cumulative History Analysis for Bistable Perception Time Series
Evaluates values for a fixed history parameter
Checks for validity of values for use as normal distribution parameter...
Extract Model Coefficients
Computes cumulative history for the time-series
Class cumhist.
Evaluates validity of initial history values.
Evaluates whether and how to fit a cumulative history parameter.
Computes history for a fitted model
Extracts a history parameter as a matrix
Extract a term and replicates it randomN times for each linear model
Extracts a term with one column per fixed or random-level into a matri...
Computes cumulative history
Fits cumulative history for bistable perceptual rivalry displays.
Extract the fixed-effects estimates
Extract values of used or fitted history parameter mixed_state
Extract values of used or fitted history parameter
Extract values of used or fitted history parameter tau
Extract the history-effects estimates
Computes an efficient approximate leave-one-out cross-validation via l...
Computes predicted dominance phase durations using posterior predictiv...
Computes predicted cumulative history using posterior predictive distr...
Computes prediction for a each sample.
Preprocesses time-series data for fitting
Prints out cumhist object
Summary for a cumhist object
Computes widely applicable information criterion (WAIC).
Estimates cumulative history for time-series for continuously viewed bistable perceptual rivalry displays. Computes cumulative history via a homogeneous first order differential process. I.e., it assumes exponential growth/decay of the history as a function time and perceptually dominant state, Pastukhov & Braun (2011) <doi:10.1167/11.10.12>. Supports Gamma, log normal, and normal distribution families. Provides a method to compute history directly and example of using the computation on a custom Stan code.
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