mvregresslike
Negative log-likelihood for multivariate regression
Syntax
nlogL = mvregresslike(X,Y,b,SIGMA,alg)
[nlogL,COVB] = mvregresslike(...)
[nlogL,COVB] = mvregresslike(...,type,format)
Description
nlogL = mvregresslike(X,Y,b,SIGMA, computes
the negative log-likelihood alg)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 bySIGMA'mvn'— Multivariate normal estimates computed after omitting rows with any missing values inY
[nlogL,COVB] = mvregresslike(...) also
returns an estimated covariance matrix COVB of
the parameter estimates b.
[nlogL,COVB] = mvregresslike(..., specifies
the type and format of type,format)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 computeCOVBforbonly. This is the default.'full'— To computeCOVBfor bothbandSIGMA.
Version History
Introduced in R2007a
