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

Description

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

Usage

1

Format

A data frame with 500 rows and 6 variables

Details

The variables are as follows:


Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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