bdfa: Bayesian Dynamic Factor Analysis
In vsartor/bnsa: Bayesian Non-Structured Analysis
Description
Usage
Arguments
Value
Description
Simulates from the posterior distribution of the parameters of a
simple Bayesian Dynamic Factor Analysis using the Gibbs algorithm,
considering vague prior distributions for all parameters and a random
walk evolution for the factors.
Usage
1
bdfa(Y, k, N = 5000)
Arguments
Y
A T by m matrix containing each m-dimensional observation
in each row.
k
The number of factors to be considered.
N
The number of iterations for the MCMC algorithm to run.
Value
A list with the chains 'factor', 'beta', 'vars' and 'lambda'.
vsartor/bnsa documentation built on May 17, 2019, 12:04 p.m.
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Description Usage Arguments Value
Description
Simulates from the posterior distribution of the parameters of a simple Bayesian Dynamic Factor Analysis using the Gibbs algorithm, considering vague prior distributions for all parameters and a random walk evolution for the factors.
Usage
1 | bdfa(Y, k, N = 5000)
|
Arguments
Y |
A T by m matrix containing each m-dimensional observation in each row. |
k |
The number of factors to be considered. |
N |
The number of iterations for the MCMC algorithm to run. |
Value
A list with the chains 'factor', 'beta', 'vars' and 'lambda'.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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