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This may influence the implementation of imputation from various lower density chips to HD chips in routine operations.
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We have shown that implementations of imputation procedures for categorical variables can go seriously wrong if perfect prediction occurs.
The results support the implementation of multiple imputation methods in the treatment of missing data in background information.
Consequently, routine implementation of marker imputation provides a powerful tool for marker-assisted wheat improvement.
The implementation of all imputation methods is demonstrated in File S6.
Any deficiencies in the assumptions and implementation of the imputation model will only affect the incomplete component of the dataset and not the whole sample [ 38].
In practice, implementations of multiple imputation in statistical software are made under the MAR assumption.
In this paper, we compare a standard implementation of MICE with imputation using missForest, and we propose a new version of MICE which imputes each variable using random forest.
These results indicate that imputation from low marker density to high marker density, shown by Hickey et al. (2012) to be very accurate in maize, is important in the practical implementation of GS, because imputation from low to high marker density will generate economic savings during the breeding cycles while increasing prediction accuracy.
RF imputation The implementation of RF by (Breiman and Cutler, 2004 ) imputes missing data by initializing each missing value to the mean of the feature and re-estimating values by building a forest and using nearest examples from the leaf nodes.
Thus, the degree of missing data was relatively small, and did not warrant the implementation of methods such as multiple imputation.
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