Sentence examples for multivariate approaches taking from inspiring English sources
Although the statistical tests describe the differences among groups and produce features that can potentially lead to the classification of the groups under consideration, it is often the case that multivariate approaches, taking into account combinations and interactions of features, rather than univariate approaches, lead to better results.
The explanation to this paradox may lie in the multivariate approach taken here (and in Anzak et al., 2012), which highlights those LFP features that continue to predict behavioural performance while other features are held constant.
Specifically, we considered both univariate approaches that evaluate the relevance of each single gene independently from the others and multivariate approaches that take into account interdependencies among genes.
In particular, we considered both univariate approaches, where each feature is ranked individually, and multivariate approaches that take into account feature dependencies.
A multivariate approach was taken in an effort to minimize the bias associated with nonrandomization.
The sample size did not allow a multivariate approach that takes into account the potential confounding factors.
Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression.
A multilevel approach was used to investigate the relationships between the repeated measurements while taking advantage of multivariate approaches.
Because of the complexity of processes that can operate on populations at range edges and the diversity of traits involved, taking a multivariate approach to the study of range edges should aid our understanding of their evolution and maintenance [ 4].
Like other multivariate approaches (e.g., PCA and ICA), MVPA takes into account multivoxel patterns of brain activity or connectivity.
In this multivariate approach two variables were taken into account (location: supratentorial vs infratentorial, i.e. M1 + M2 vs M3 + M4, and radiology: cystic vs solid, i.e. R1 + R2 vs R3 + R4).
