BayesianDynamicSystemModeling: Bayesian Dynamic System Modeling
Description
bdynsys is a package for panel/longitudinal data that combines methods to model
changes in up to four indicators over times as a function of the indicators itself and up to three predictors using ordinary differential equations (ODEs) with polynomial terms that allow to model complex and nonlinear effects. A Bayesian model selection approach is implemented. The package provides also visualisation tools to plot phase portraits of the dynamic system, showing the complex co-evolution of two indicators over time with the possibility to highlight trajectories for specified entities (e.g. countries, individuals). bdynsys is also the
name of the main function in the bdynsys package, that performs the bayesian dynamic
systems modeling.
Usage
bdynsys(dataset, indnr, paramnr, x, y, z, v)
Arguments
dataset
a plm, apdata.frame panel data frame.
indnr
an integer number indicating number of indicators,
to be included in the modeling procedure
paramnr
an integer number indicating number of modelparameters,
this is the maximum number of polynomial terms included.
x
a reference to variable from the paneldata to be included
as indicator 1 in the modeling procedure.
y
a reference to variable from the paneldata to be included
as indicator 2 in the modeling procedure.
z
a reference to variable from the paneldata to be included
as indicator 3 in the modeling procedure.
v
a reference to variable from the paneldata to be included
as indicator 4 in the modeling procedure.
References
Ranganathan, S./Spaiser, V./Sumpter, D.J.T. (2013)
A Bayesian approach to modeling dynamical systems in the social sciences.
SIMULTECH 2013 Proceedings, Reykjavik.
## Bayesian Dynamic System Modeling with two variables and three modelparameters## dx = + 0.0012 /x^2 and dy = + 0.0071 x^3bdynsys(datap, 2, 3, datap$logGDP, datap$EmanzV)