mvnrobj
Log-likelihood function for multivariate normal regression without missing data
Syntax
Objective = mvnrobj(Data,Design,Parameters,Covariance,CovarFormat)
Arguments
|
|
| A matrix or a cell array that handles two model structures:
|
|
|
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| (Optional) Character vector that specifies the format for the covariance matrix. The choices are:
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Description
Objective = mvnrobj(Data,Design,Parameters,Covariance,CovarFormat)
computes
the log-likelihood function based on current maximum likelihood parameter
estimates without missing data. Objective
is a
scalar that contains the log-likelihood function.
Notes
You can configure Design
as a matrix if NUMSERIES
= 1
or as a cell array if NUMSERIES
≥ 1
.
If
Design
is a cell array andNUMSERIES
=1
, each cell contains aNUMPARAMS
row vector.If
Design
is a cell array andNUMSERIES
>1
, each cell contains aNUMSERIES
-by-NUMPARAMS
matrix.
Although Design
should not have NaN
values,
ignored samples due to NaN
values in Data
are
also ignored in the corresponding Design
array.
Examples
See Multivariate Normal Regression, Least-Squares Regression, Covariance-Weighted Least Squares, Feasible Generalized Least Squares, and Seemingly Unrelated Regression.
Version History
Introduced in R2006a