Documentation

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 NaNs, 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) String 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. 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 and NUMSERIES = 1, each cell contains a NUMPARAMS row vector.

  • If Design is a cell array and NUMSERIES > 1, each cell contains a NUMSERIES-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.

Introduced in R2006a

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