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
data(EMGsim)
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
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