Claim · model-written summary
With synthetic covariates fitted, the rank correlation is 0.58 for the factor analytic variance-covariance structure.
SorghumNot reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e3“When the SC was fitted, the rank correlation was 0.58 for the factor analytic, 0.51 for the diagonal and 0.46 for the identity variance-covariance structures”
e4“Our approach indicates improvement in predictive accuracy with SC in the context of genotype-by-location interactions of a sorghum breeding in Ethiopia”
Title
Diriba Tadese; Hans‑Peter Piepho; Jens Hartung Theoretical and Applied Genetics 2024. doi:10.1007/s00122-024-04684-z
Other claims from this paper (4)
- Method / resource Models with synthetic covariates improve predictive accuracy compared with models without them in three of the variance-covariance structures.
- Method / resource The rank correlation is higher for the model with synthetic covariates than for models without them.
- Method / resource With synthetic covariates fitted, the rank correlation is 0.51 for the diagonal variance-covariance structure.
- Method / resource With synthetic covariates fitted, the rank correlation is 0.46 for the identity variance-covariance structure.