WOMBAT Example 13

This example further illustrates multivariate analyses with repeated records
per trait. 
 
New features: 
1) WOMBAT now insists that the option RPTCOV is specified in the parameter 
   file!
2) WOMBAT writes out a file RepeatedRecordsCounts with some basic information
   on how many animals have how many records.
3) There is now a mechanism -- through RPTCOV TSELECT -- to specify which 
   records are measured at the same time and thus have a non-zero error
   covariance and which are not.
4) Trait number need to be assigned so that any traits with repeated records
   have a lower number than traits with single records.

The data are simulated records - obtained by simulating records for 4
traits recorded on 800 animals at 5 different times. A missing value
indicator (999) is used to create different pattern of missing records
- note that analyses in the different sub-directories analyze different 
columns in the data file.

A:  Demonstrates an analysis without missing records, i.e. where all
    traits are recorded at the same time. This implies that there are non-zero
    error covariances between all traits and that the ALIGNED option is 
    appropriate.

B:  Shows the analysis when some records are missing, but in a systematic
    fashion: Traits 1 and 2 have records at all 5 times, but traits 3 and 4
    are only recorded for times 1 and 2. As the `missing' observations only
    occur for the later times, 3 to 5, the option ALIGNED is still appropriate.

C:  Similar to B, but measurements for traits 3 and 4 are taken at times 2 
    and 4. This means that a time of recording indicator needs to be used
    to model the residual covariance structure correctly. This is done
    specifying TSELECT together with the name of the column in the data file
    which contains the time variable.

C1: As C, but using a multivariate random regression analysis.

C2: As C, but supplying the relationship matrix as *.gin file and carying
    out a reduced rank analysis. Illustrates the use of a *.matrix file to
    enable estimation using the AI algorithm.

D:  Illustrates the scenario where we have a trait with repeated records
    analysed together with traits with single records and where traits with
    single and repeated records are measured at different times so that 
    a) there are no error covariances between these groups of traits and 
    b) that we can `use' the error covariance to model covariances between 
       traits due to permanent environmental effects of the animal.
    For this example, we use records taken at times 1 to 4 for trait 1, and
    records taken at time 5 for traits 2 to 4.
    For this case a model fitting a permanent environmental effects due to
    the animal for trait 1 only together with the INDESCR option is appropriate.
    Estimates of the error covariances betwen trait 1 and traits 2, 3 and 4 then
    reflect the permanent environmental covariance, while estimates of the
    (co)variances among the latter represent the sum of temporary and permanent
    environmental covariances.

E:  Shows the case where we have a trait with repeated records analysed together
    with traits with single records, but where the single records are taken at
    the same time as one of the repeated records, so that we need to model
    non-zero error covariances.
    Here we consider records for trait 1 at all 5 times, and records for traits
    2 to 3 taken at time 5.
    Again we need the TSELECT option to model this properly. In addition, we
    need to use the equivalent model invoked via the PEQ option in order to
    separate temporary and permanent environmental covariances between trait 1 
    and the other traits. Note that permanent environmental effects are fitted
    for all 4 traits, but that only the corresponding covariance components 
    which can be disentangled from the environmental covariances are reported.

