Sentence examples for multivariate space to from inspiring English sources

Indices I1 to I4 use multivariate space to estimate niche breadth and relative position.

This was done to verify that the significant outcome in PERMANOVA was due to differences in location in multivariate space, not to differences in dispersion among the groups.

Both species and functions differed statistically (MANOVA, species: Pillai's trace = 0.91, df = 27, p < 0.001; function: Pillai's trace = 0.29, p < 0.001) but the interaction of species and function did not (Pillai's trace = 0.02, p = 0.99), indicating that protein functional groups occupied similar regions of multivariate space relative to each other within each species' proteome.

In the first approach, the constraining effect of the correlation between traits is assessed using artificial selection, by imposing selection regimes that would "push" the phenotype in multivariate space at right or near-right angles to g max, i.e., along the hypothetical line of greatest evolutionary resistance (reviewed in [ 21]).

Finally, evolutionary distance relates to functional distance along a single trait axis or within a multivariate space at species-pairwise level, which can result in more intense competition for resources between closely related than between more distantly related species (Cahill et al. 2008; Cadotte et al. 2013).

The five most highly ranked, and thus most cohesive in multivariate space, genetic clusters corresponded either to single morphospecies or groups of morphospecies.

In other words, we aimed to find in multivariate space whether the patterns of composition were indeed similar across languages.

Additionally, to find in multivariate space whether the patterns of composition were indeed similar across languages, we ran a representational similarity analysis (RSA), in which we tested for a correlation between the actual data and a model in which the contrast between the phrase and list stimuli was identical for the two languages.

Phenotypic diversity was estimated for each colony and treatment as the mean distance to the group centroid in multivariate space [ 50].

The data generated from 44 C. alismatifolia individual plants were subjected to principal component analysis (PCA) to visualize individuals in a multivariate space.

Here, we develop three new metrics that can be used to identify outliers in multivariate space, while making no strong assumptions about the distribution of the data.