EMGsim: Simulated single-subject time series to capture features of...

EMGsimR 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:

  • id. ID of the participant (= 1 in this case, over 500 time points)

  • EMG. Hypothetical observed facial electromyograhy data

  • self. Covariate - the individual's concurrent self-reports

  • truestate. The true score of the individual's EMG at each time point

  • trueregime. The true underlying regime for the individual at each time point

dynr documentation built on Oct. 17, 2022, 9:06 a.m.