Mstep: Maximizing expected likelihood using analytical solution
In ldsr: Linear Dynamical System Reconstruction
Description
Usage
Arguments
Value
Description
Maximizing expected likelihood using analytical solution
Usage
1
Mstep(y, u, v, fit)
Arguments
y
Observation matrix (may need to be normalized and centered before hand) (q rows, T columns)
u
Input matrix for the state equation (m_u rows, T columns)
v
Input matrix for the output equation (m_v rows, T columns)
fit
result of Kalman_smoother
Value
An object of class theta: a list of
ldsr documentation built on May 4, 2020, 5:06 p.m.
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Description Usage Arguments Value
Description
Maximizing expected likelihood using analytical solution
Usage
1 | Mstep(y, u, v, fit)
|
Arguments
y |
Observation matrix (may need to be normalized and centered before hand) (q rows, T columns) |
u |
Input matrix for the state equation (m_u rows, T columns) |
v |
Input matrix for the output equation (m_v rows, T columns) |
fit |
result of Kalman_smoother |
Value
An object of class theta: a list of
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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