mvnrobj

Log-likelihood function for multivariate normal regression without missing data

Syntax

Objective = mvnrobj(Data,Design,Parameters,Covariance,CovarFormat)

Arguments

`Data`	`NUMSAMPLES`-by-`NUMSERIES`matrix with`NUMSAMPLES`samples of a`NUMSERIES`-dimensional random vector. If a data sample has missing values, represented as`NaN`s, the sample is ignored. (Use`ecmmvnrmle`to handle missing data.)
`Design`	A matrix or a cell array that handles two model structures: If`NUMSERIES = 1`,`Design`is a`NUMSAMPLES`-by-`NUMPARAMS`matrix with known values. This structure is the standard form for regression on a single series. If`NUMSERIES`≥`1`,`Design`is a cell array. The cell array contains either one or`NUMSAMPLES`cells. Each cell contains a`NUMSERIES`-by-`NUMPARAMS`matrix of known values. If`Design`has a single cell, it is assumed to have the same`Design`matrix for each sample. If`Design`has more than one cell, each cell contains a`Design`matrix for each sample.
`Parameters`	`NUMPARAMS`-by-`1`column vector of estimates for the parameters of the regression model.
`Covariance`	`NUMSERIES`-by-`NUMSERIES`matrix of estimates for the covariance of the residuals of the regression.
`CovarFormat`	(Optional) Character vector that specifies the format for the covariance matrix. The choices are: `'full'`— Default method. The covariance matrix is a full matrix. `'diagonal'`— The covariance matrix is a diagonal matrix.

Description

Objective = mvnrobj(Data,Design,Parameters,Covariance,CovarFormat)computes the log-likelihood function based on current maximum likelihood parameter estimates without missing data.Objectiveis a scalar that contains the log-likelihood function.

Notes

You can configureDesignas a matrix ifNUMSERIES = 1or as a cell array ifNUMSERIES≥1.

IfDesignis a cell array andNUMSERIES=1, each cell contains aNUMPARAMSrow vector.
IfDesignis a cell array andNUMSERIES>1, each cell contains aNUMSERIES-by-NUMPARAMSmatrix.

AlthoughDesignshould not haveNaNvalues, ignored samples due toNaNvalues inDataare also ignored in the correspondingDesignarray.

Examples

SeeMultivariate Normal Regression,Least-Squares Regression,Covariance-Weighted Least Squares,Feasible Generalized Least Squares, and看似不相关的注册ression.

版本历史

Introduced in R2006a