EMGsim {dynr}R Documentation

Simulated single-subject time series to capture features of facial electromyography data


A dataset simulated using an autoregressive model of order (AR(1)) with regime-specific AR weight, intercept, and slope for a covariate. This model is a special case of Model 1 in Yang and Chow (2010) in which the moving average coefficient is set to zero.

Reference: Yang, M-S. & Chow, S-M. (2010). Using state-space models with regime switching to represent the dynamics of facial electromyography (EMG) data. Psychometrika, 74(4), 744-771




A data frame with 500 rows and 6 variables


The variables are as follows:

[Package dynr version 0.1.16-27 Index]