WOMBAT – A program for Mixed Model Analyses by Restricted Maximum Likelihood

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wombat:ex13page [2018/07/02] (current)
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 +====== Example 13 for WOMBAT ======
  
 +Download [[http://​didgeridoo.une.edu.au/​km/​download.php?​file=example13.tar.gz|example13.tar.gz]]
 +
 +This example illustrates multivariate analyses with repeated records
 +per trait -- this can be tricky to model if same records for different traits are taken at the same time and others are not.
 + 
 +New features have been implemented to assist with this:
 +  -  WOMBAT now insists that the option ''​RPTCOV''​ is specified in the parameter ​   file!
 +  - WOMBAT writes out a file <​nowiki>''​RepeatedRecordsCounts''</​nowiki>​ with some basic information ​   on how many animals have how many records.
 +  - 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.
 +  -  Trait numbers 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, 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.
 +
 +  * **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\\ ​
 +      - there are no error covariances between these groups of traits and 
 +      - that we can `use' the error covariance to model covariances between ​        ​traits due to permanent environmental effects of the animal.  ​
 +<​html>&​nbsp &nbsp &​nbsp</​html> ​  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 between 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 nvironmental 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.
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