Package dsem fits dynamic structural equation models, which includes as nested submodels:
- structural equation models
- vector autoregressive models
- dynamic factor analysis
- state-space autoregressive integrated moving average (ARIMA) models
The model has several advantages:
- It estimates direct, indirect, and total effects among system variables, including simultaneous and lagged effects and recursive (cyclic) dependencies
- It can estimate the cumulative outcome from press or pulse experiments or initial conditions that differ from the stationary distribution of system dynamics
- It estimates structural linkages as regression slopes while jointly imputing missing values and/or measurement errors
- It is rapidly fitted as a Gaussian Markov random field (GMRF) in a Generalized Linear Mixed Model (GLMM), with speed and asymptotics associated with each
- It allows granular control over the number of parameters (and restrictions on parameters) used to structure the covariance among variables and over time,
dsem is specifically intended as a minimal implementation, and uses standard packages to simplify input/output formatting:
- Input: time-series defined using class ts, with
NAfor missing values - Input: structural trade-offs specified using syntax defined by package sem
- Output: visualizing estimated trade-offs using igraph
- Output: access model output using standard S3-generic functions including
summary,predict,residuals,simulate, andAIC
Please see package vignettes for more details regarding syntax and features.