Simulated multi-subject time series based on a dynamic factor analysis model with nonlinear relations at the latent level



A dataset simulated using a discrete-time nonlinear dynamic factor analysis model with 6 observed indicators for identifying two latent factors: individuals' positive and negative emotions. Proposed by Chow and Zhang (2013), the model was inspired by models of affects and it posits that the two latent factors follow a vector autoregressive process of order 1 (VAR(1)) with parameters that vary between two possible regimes: (1) an "independent" regime in which the lagged influences between positive and negative emotions are zero; (2) a "high-activation" regime to capture instances on which the lagged influences between PA and NA intensify when an individual's previous levels of positive and negative emotions were unusually high or low (see Model 2 in Chow & Zhang).




A data frame with 3000 rows and 8 variables


Reference: Chow, S-M, & Zhang, G. (2013). Regime-switching nonlinear dynamic factor analysis models. Psychometrika, 78(4), 740-768.

  • id. ID of the participant (1 to 10)

  • time. Time index (300 time points from each subject)

  • y1-y3. Observed indicators for positive emotion

  • y4-y6. Observed indicators for negative emotion

Want to suggest features or report bugs for Use the GitHub issue tracker.