Evaluate standard errors for multivariate normal regression model
[StdParameters, StdCovariance] = ecmmvnrstd(Data, Design,
Covariance, Method, CovarFormat)
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 mvnrstd.)
A matrix or a cell array that handles two model structures:
NUMSERIES-by-NUMSERIES matrix of estimates for the covariance of the regression residuals.
(Optional) String that identifies method of calculation for the information matrix:
(Optional) String that specifies the format for the covariance matrix. The choices are:
[StdParameters, StdCovariance] = ecmmvnrstd(Data, Design, Covariance, Method, CovarFormat) evaluates standard errors for a multivariate normal regression model with missing data. The model has the form
for samples k = 1, ... , NUMSAMPLES.
ecmmvnrstd computes two outputs:
StdParameters is a NUMPARAMS-by-1 column vector of standard errors for each element of Parameters, the vector of estimated model parameters.
StdCovariance is a NUMSERIES-by-NUMSERIES matrix of standard errors for each element of Covariance, the matrix of estimated covariance parameters.
Note ecmmvnrstd operates slowly when you calculate the standard errors associated with the covariance matrix Covariance.
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.