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- Asreml r individual variance parameters convergence how to#
- Asreml r individual variance parameters convergence code#
How to get starting values is the second question (after how to define covariance structures) when starting to run multivariate analyses. Top of page Starting values: the apocryphal Dutch method Now dbhS1, which contains 1s or 0s, is multiplied by the dbhs (!*V5) and the 0s are treated as missing values (!M 0). If the result of the comparison is TRUE, the variable takes a value of 1, 0 otherwise.
Asreml r individual variance parameters convergence code#
Then the value is compared to the site code (e.g. How does it work? First the site codes are copied to, say, dbhS1 (!=V2). In this case, dbhS1, dbhS2 and dbhS3 correspond to dbhs measures at sites 1, 2 and 3 respectively. Site 3 # Three sites with codes 1, 2 and 3 V2 It is possible to expand the dataset to have as many variables for dbh as there are sites.Ĭreating multiple variables from one variable For example, we are interested in dbh in several sites, so for each record we have only one dbh in one site. Sometimes the dataset is setup in a univariate way, but we are interested in using some multivariate structures that are easier to fit when the data appear as several traits. Top of page Univariate to multivariate format Finally, the 0s are converted to missing values (!M0). Later newdbh is multiplied by dbh04 (!*V4), taken the value of dbh04 for the 1s and 0 for the 0s. If newdbh is greater than or equal to $A, it takes a value of 1 (TRUE), 0 otherwise (FALSE). So, how does it work? First, variable number 4 (dbh04) is copied to newdbh (!=V4) then, ASReml compares its value with the value taken by $A (!≥$A, 5 cm in this case). Would keep all trees with more than 5 cm of diameter and run part 1. To run the previous example, assuming the file is called testrunts.as, you would need to specify to specify two numbers in the command line: the first one referring to $A (minimum diameter to include in the analysis) and the second referring to $B (part of the analysis to be run), e.g. Lets say that you want to test the effect of dropping these trees from the analysis, but you are not sure where is the limit of size to drop the trees. The presence of runts (individual with extremely poor growth) is common in progeny trials. Below you will find some (hopefully) useful examples. Starting values: the apocryphal Dutch methodĪS Reml has not really been designed to perform data management (better use a database system - Access, SAS, Oracle, MySQL, your pick), but still allows you to perform a few variable conversions, subsetting, etc.