Co-variance matrices for all random factors in the models including the residual can be specified.
This can be directly in the driver file following the $PRIOR keyword, or in a file pointed to by the
fn option of the $PRIOR keyword line.
For task=1 (DMUAI), the values specified are used as starting values. If no starting values are specified,
an identity matrix is assumed for all (co)variance matrices in the model.
For task=2 (RJMC), the values are used as priors with a degree of belief as specified in the additional
input to RJMC, using the $RJMC keyword.
For task=11 and 12 all non-zero elements in all (co)variance matrices must be specified, and are used in
the model as "true" values.
The matrices are numbered as in the RANDOM model directive line. The number for the residual (co)variance
matrix is always one larger than the last factor in the RANDOM line. DMU will print a summary of the assumed
covariance structure, which can be used to check that priors are correctly specified.
The prior variances and covariances must be specified in random factor number sequence i.e. priors for random
factor 1 must be specified before priors for random factor 2 and so on. Each line consists of 3 integers and a
real number (free format). The first integer is the random factor number followed by row-column (trait)
combination and finally the prior (co)variance.
For estimation of variance compoments (task 1 or 2) in models with heterogenious residual variance general
starting values to used for all residual variance groups can be specified, or specific vales for each variance
group can be specified. In this case, the lines with starting values for the residual co-variances must have
an extra column containing the code for the heterogen residual variance group.
For prediction (task 11 or 12), the "true" co-variance components must be specified for all heterogen residual
variance group, and values for the residual co-variances must have an extra column containing the code for the
heterogen residual variance group.