Sentence examples for multivariate methods that from inspiring English sources

There are difficulties, however, in generalising simple univariate definitions of interaction, from classic linear models, to the robust, non-parametric multivariate methods that are commonly required in handling assemblage data.

In contrast, beta diversity measures (dis)similarity among samples through the use of multivariate methods that compare samples based on taxon composition and relative abundance.

It can however be quantified as the number of trophic levels, functional groups, life cycles, and by the resources used by species [3], [4], or using multivariate methods that summarize the functional variability in the group of species being analyzed [8] [11].

The problem is mitigated somewhat by multivariate methods that utilize locality or other inference.

Hence, as has been suggested recently by Abrams et al. (2012), inconsistencies in the literature may be reconciled by the use of more sensitive multivariate methods that allow the identification of a wider intelligibility network.

Instead of examining the association of each rare variant in isolation, multivariate methods that combine information across multiple variant sites within a gene or other functional genomic region are a viable alternative strategy (Fig.  1).

This analysis is a multivariate method that maximizes shared structure among multiple datasets drawn from the same samples.

Principal components analysis is a multivariate method that decomposes the correlations or co-variances between all variables into subsets that best capture the total variability in the data.

PCA is an unsupervised multivariate method that allows patterns, trends, groups and outliers in a large datasets to be easily identified.

Logistic regression is another commonly used multivariate method that has been applied to many bioinformatics tasks for both classification and feature ranking.

DA is a supervised multivariate method that calculates the combination of variables (in this case PCs) which gives maximum separation between the pre-defined groups.