To maximise our statistical power, we used multiple imputation in SPSS 19.0 to impute missing FSM data (Table 1) and the individual diet variables described below.
Missing data was handled by performing multiple imputation in which the candidate explanatory factors previously described were imputed on the basis of each other using chained imputation [ 19].
Thus, no imputation in the solution dominates another imputation in the solution.
Friedenberg, S. G. & Meurs, K. M. Genotype imputation in the domestic dog.
Fast and accurate genotype imputation in genome-wide association studies through pre-phasing.
Accordingly, this provides insights into the effectiveness of dividend imputation in mitigating corporate tax avoidance, as well as providing support for the continuance of dividend imputation in Australia.
The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset.
Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing.
RMSE values < 40% can be considered a good imputation in light of the low density of points used in this study.
A reference panel was selected from the 360 re-sequenced tomato accessions19 to perform SNP imputation in panels S and B. Among this panel, only accessions with genome coverage ≥90% and mean sequencing depth ≥4.0 were kept.
Specifically, we ask the question of how to optimally select a subset of a study sample for sequencing when choosing an internal reference panel for imputation, in order to maximize the eventual imputation accuracy.
