Sentence examples for partitioning of variance from inspiring English sources

Partitioning of variance within and among populations was estimated using Arlequin v3.1 by analysis of molecular variance (AMOVA) with 10,000 permutations.

Partitioning of variance components of clinical, sample and experimental metadata revealed the largest components of array hybridization variability to be metrics of mRNA quality, in accord with recent studies of post-mortem brain tissue [70].

While this does not allow for the hierarchical partitioning of variance, the advantage of this method over AMOVA is that it uses an explicit linear model and does not require an a priori user-defined population structure.

Due to the partitioning of variance into common and trait-specific variance, the number of factors needed to explain the variability in the data is normally notably smaller than the number of PC needed in the PC approach.

Failure to account for these correlations may produce unstable regression coefficients that are influenced by outlying points and may not be readily interpretable for certain combinations of foods due to the arbitrary partitioning of variance between them.

Additionally, an AMOVA was performed in order to assess the partitioning of variance between the Lineage E (primarily distributed in the eastern part of the Pyrenees) and the rest of lineages (see below).

Partition of variance analysis was used to evaluate the variance associated with latitude and longitude (one-degree bands) and host plant (juniper or arborvitae).

Partition of variance analysis indicated that the variance component associated with latitude was consistently larger than that associated with longitude or host plant for all parameters, usually by a factor >5 (Table 2).

For a given partition of variance between linear (L), nonlinear (NL and IIDD error ε, how much data n ⋆ is required before complete selection of causal variants occurs (i.e., crossing of the phase boundary for algorithm performance)?

Tests of significance and partitions of variance components for the RIL, year and RIL × year effects were carried out based on a Model II ANOVA, where the RIL and year effects were treated as random variables (Sokal and Rohlf, 1981).

The standard varieties used to connect the different blocks enabled estimation of the inter-block variance and partitioning of the variance into block and error components.