Sentence examples for missing data shared from inspiring English sources

However, rather than based solely on the amount of missing data artificial signal for a grouping of taxa might also stem from a strong degree of overlap in missing data shared between taxa, if the missing data are not randomly distributed across the taxa, but are systematically biased [ 54- 56].

Hierarchical clustering analyses based on the degree of overlap in missing data shared between taxa (Additional file 1: Figure S1 and Additional file 2: Figure S2) corroborate that neither Lophophorata nor Ectoprocta + Phoronida are artifacts caused by shared missing data.

Furthermore, we investigated the influence of shared missing data on phylogenetic reconstruction by hierarchical clustering analyses based on the degree of overlap in missing data between taxa using BaCoCa version 1.105 [ 85].

This can also been seen in density distributions of shared missing data, which is strongly shifted to lower values in the reduced dataset (compare Additional file 1: Figure S1 and Additional file 2: Figure S2).

TCS v1.21 [ 83] was used to assign individuals to haplotypes, with attention to ensure that sequences that shared missing data at a given nucleotide position were not assigned to different haplotypes.

Due to the size of this dataset many taxa suffer from large amount of missing data, but all share at least one mitochondrial or nuclear gene, thus avoiding the problem of non overlapping sequences [ 142].

Since there are missing data on the share of international linkages that are regional rather than global, it is impossible to distinguish globalization from internationalisation and regionalisation with complete certainty.

We acknowledge that it is highly debatable to impute large shares of missing data, but we argue that it is reasonably safe to assume these values to be missing at random because the missingness is design-based and not due to (possibly selective) non-response.

Moreover this study shares the limitation of all surveillance systems: underreporting, missing data, timelessness and regional differences in health system.

However, our study shares the disadvantages of most retrospective studies, including missing data issues [ 43], lack of randomization, and a potential for model misspecification.

The SHARE engine [60] decides which services to invoke in order to fill in the missing data.