This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.


Negative log-likelihood for multivariate regression


nlogL = mvregresslike(X,Y,b,SIGMA,alg)
[nlogL,COVB] = mvregresslike(...)
[nlogL,COVB] = mvregresslike(...,type,format)


nlogL = mvregresslike(X,Y,b,SIGMA,alg) computes the negative log-likelihood nlogL for a multivariate regression of the d-dimensional multivariate observations in the n-by-d matrix Y on the predictor variables in the matrix or cell array X, evaluated for the p-by-1 column vector b of coefficient estimates and the d-by-d matrix SIGMA specifying the covariance of a row of Y. If d = 1, X can be an n-by-p design matrix of predictor variables. For any value of d, X can also be a cell array of length n, with each cell containing a d-by-p design matrix for one multivariate observation. If all observations have the same d-by-p design matrix, X can be a single cell.

NaN values in X or Y are taken as missing. Observations with missing values in X are ignored. Treatment of missing values in Y depends on the algorithm specified by alg.

alg should match the algorithm used by mvregress to obtain the coefficient estimates b, and must be one of the following:

  • 'ecm' — ECM algorithm

  • 'cwls' — Least squares conditionally weighted by SIGMA

  • 'mvn' — Multivariate normal estimates computed after omitting rows with any missing values in Y

[nlogL,COVB] = mvregresslike(...) also returns an estimated covariance matrix COVB of the parameter estimates b.

[nlogL,COVB] = mvregresslike(...,type,format) specifies the type and format of COVB.

type is either:

  • 'hessian' — To use the Hessian or observed information. This method takes into account the increased uncertainties due to missing data. This is the default.

  • 'fisher' — To use the Fisher or expected information. This method uses the complete data expected information, and does not include uncertainty due to missing data.

format is either:

  • 'beta' — To compute COVB for b only. This is the default.

  • 'full' — To compute COVB for both b and SIGMA.

Introduced in R2007a

Was this topic helpful?