Description Usage Arguments Details Value Author(s) References See Also
Computes the expected value of random variables involving Y for the EM algorithm. This function is not exported. Users should use print( MLEobj, what="Ey")
to access this output. See print.marssMLE
.
1 | MARSShatyt( MLEobj )
|
MLEobj |
A |
For state space models, MARSShatyt()
computes the expectations involving Y. If Y is completely observed, this entails simply replacing Y with the observed y. When Y is only partially observed, the expectation involves the conditional expectation of a multivariate normal.
A list with the following components (n is the number of state processes). Following the notation in Holmes (2012), y(1) is the observed data (for t=1:TT) while y(2) is the unobserved data. y(1,1:t) is the observed data from time 1 to t.
ytT |
Estimates E[Y(t) | Y(1,1:TT)=y(1,1:TT)] (n x T matrix). |
ytt1 |
Estimates E[Y(t) | Y(1,1:t-1)=y(1,1:t-1)] (n x T matrix). |
OtT |
Estimates E[Y(t) t(Y(t) | Y(1)=y(1)] (n x n x T array). |
yxtT |
Estimates E[Y(t) t(X(t) | Y(1)=y(1)] (n x m x T array). |
yxt1T |
Estimates E[Y(t) t(X(t-1) | Y(1)=y(1)] (n x m x T array). |
errors |
Any error messages due to ill-conditioned matrices. |
ok |
(T/F) Whether errors were generated. |
Eli Holmes, NOAA, Seattle, USA.
eli(dot)holmes(at)noaa(dot)gov
Holmes, E. E. (2012) Derivation of the EM algorithm for constrained and unconstrained multivariate autoregressive state-space (MARSS) models. Technical report. arXiv:1302.3919 [stat.ME] Type RShowDoc("EMDerivation",package="MARSS")
to open a copy.
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