mvregresslike
R2026bNegative 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,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 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(...,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 computeCOVBforbonly. This is the default.'full'— To computeCOVBfor bothbandSIGMA.
Version History
Introduced in R2007a
