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README
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.
Citation
Thorson, J. T., Andrews, A. G., Essington, T., & Large, S. (2024). Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms. Methods in Ecology and Evolution 15(4): 744-755. https://doi.org/10.1111/2041-210X.14289
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