Log-likelihood function for multivariate normal regression without missing data
Objective = mvnrobj(Data, Design, Parameters, Covariance,
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.)
A matrix or a cell array that handles two model structures:
NUMPARAMS-by-1 column vector of estimates for the parameters of the regression model.
NUMSERIES-by-NUMSERIES matrix of estimates for the covariance of the residuals of the regression.
(Optional) String that specifies the format for the covariance matrix. The choices are:
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.
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.