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

In dynr: Dynamic Models with Regime-Switching

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

data(EMGsim)

Format

A data frame with 500 rows and 6 variables

Details

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 May 29, 2024, 2:49 a.m.