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The second approach involves imputation, which treats the goal of creating a synthetic file as a large missing data problem.
In the case of large missing data, imputation techniques will be considered.
As a result of a large missing data fraction, we also reran the primary analyses using multiple imputation with SAS 18.0 to handle missing data.
Nine SNPs were excluded either due to large missing data points or ambiguity in clearly discriminating the homozygous and heterogeneous genotypes so analyses were conducted using 191 of the 200 SNPs.
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On the other hand, amplification success was greatly reduced on loci with long allele sizes (~170 bp or larger; "missing data rate" in Figure 3).
In contrast, large nuclear protein datasets of mammals with diverse taxon sampling [e.g.3], [4] show a large proportion of missing data.
To generate a large number of markers at low cost, low sequencing depth is commonly used, leading to a large proportion of missing data points.
Despite the large amount of missing data, we attempted to include oral intake and recent weight loss by treating missing data both as normal and abnormal.
Measurements from the carotid bifurcation and internal carotid artery are thought to be affected by large numbers of missing data.
Results indicate that question topic sensitivity has a large effect on missing data, and survey format has a moderate effect.
Due to large quantities of missing data, actual miles per hour traveled were not available for analysis.
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