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Dynamic structural equation
models
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
NA for 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, and AIC
Please see package vignettes for more details regarding syntax and
features.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.