Fisher information matrix for multivariate normal regression model
Fisher = ecmmvnrfish(Data, Design, Covariance, Method,
NUMSAMPLES-by-NUMSERIES matrix with NUMSAMPLES samples of a NUMSERIES-dimensional random vector. Missing values are represented as NaNs. Only samples that are entirely NaNs are ignored. (To ignore samples with at least one NaN, use mvnrfish.)
A matrix or a cell array that handles two model structures:
NUMSERIES-by-NUMSERIES matrix of estimates for the covariance of the residuals of the regression.
(Optional) String that identifies method of calculation for the information matrix:
(Optional) String that identifies parameters to be included in the Fisher information matrix:
(Optional) String that specifies the format for the covariance matrix. The choices are:
Fisher = ecmmvnrfish(Data, Design, Covariance, Method, MatrixFormat, CovarFormat) computes a Fisher information matrix based on current maximum likelihood or least-squares parameter estimates that account for missing data.
Fisher is a NUMPARAMS-by-NUMPARAMS Fisher information matrix or Hessian matrix. The size of NUMPARAMS depends on MatrixFormat and on current parameter estimates. If MatrixFormat = 'full',
NUMPARAMS = NUMSERIES * (NUMSERIES + 3)/2
If MatrixFormat = 'paramonly',
NUMPARAMS = NUMSERIES
Note ecmmvnrfish operates slowly if you calculate the full Fisher information matrix.