NonlinearDFAsim


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


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.

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Dynamic Modeling in R

NonlinearDFAsim function

A data frame with 3000 rows and 8 variables

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

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