Comments (1)
Yes, that was the original intention but I couldn't find in the literature how exactly they do that. Do they produce fitted values at every iteration and calculate new loadings using the fitted values? or do they use the BLUPs? Most sommer machinery is based on the papers from Van der Werf, Karin Meyer, or Jensen. The papers from the ASREML team are too difficult to understand, like leaving gaps intentionally for people to don't recreate their machinery. Long story short, not actively working on the package now but yes, I plan to put that approach at some point. Any clues or suggestion of a paper that does a decent job explaining methods is always welcome. Also if you can code something please feel free to make a pull request and I will add it :)
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Related Issues (20)
- predict function when spl2D is used HOT 4
- predict() error in cross-validation HOT 6
- Facing error at the stage of A.mat HOT 3
- Can't get mmec to run HOT 4
- mmec using weights (W) HOT 5
- mmer fails when number of observations > 2^15 HOT 4
- Poisson and Negative Bionomial Distributed Traits HOT 2
- how to add permanent environmental effect in random regression model ? HOT 1
- Throw error for singular system HOT 6
- GWAS entries in CHANGELOG and documentation HOT 2
- Predict.mmec function issue HOT 1
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- multivariate model specification with different fixed effects per trait HOT 1
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- Cooks distance HOT 2
- multivariate (multi-trait) for modeling GCA HOT 5
- Error in predict.mmer Error in `$<-.data.frame`(`*tmp*`, start, value = c(1, 2, 3, 4, 5, 13, : replacement has 16 rows, data has 8 HOT 2
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