EMGsim


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


datasets

Dynamic modeling of all kinds in R. These include models of
processes in discrete time or continuous time. They also include processes
that are linear or nonlinear. Latent variables can be continuous (e.g. state
space models) or discrete (e.g. regime-switching models). The general approach
involves maximum likelihood estimation of single- and multi-subject models of
latent time series with the extended Kalman filter and Kim filter. The user
provides recipes and data which are combined into a model that is then cooked to
obtain free parameter estimates.

Lu Ou

dynr

Dynamic Modeling in R

EMGsim function

A data frame with 500 rows and 6 variables

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

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