Sentence examples for multivariate dataset each from inspiring English sources

PLS-DA calculates a regression model between the multivariate dataset (each variable consisting of a m/z and retention time value) and a response variable that only contains class information (e.g. the variety classification provided by HCA).

This paper developed a new MEDE technique for multivariate dataset.

Principal Component Analysis (PCA) is a method that projects a multivariate dataset to a new coordinate system by determining the eigenvectors and eigenvalues of a matrix, facilitating visualization of the data.

To create the artificial population, a large multivariate dataset containing NFI field observations and ALS metrics was generated using a copula function fitted to the empirical observations, and then it was generalized over the study area using satellite imagery and nearest neighbor imputations.

Principal Component Analysis (PCA) was used for visualizing trends in the multivariate dataset, and identifying outliers.

Global shark biogeographic units were identified by using a hierarchical cluster analysis, a standard method used to find structure in a multivariate dataset.

The Ginkgo interface returns information on all principal coordinate axes in the dataset, and then a multivariate dataset can be plotted as axes in two dimensions for visualization [55].

Principal components analysis (PCA) is a popular technique used to find the major component of a multivariate dataset.

For instance, a principal components analysis may be correct and efficient for summarising a multivariate dataset, but may have no bearing on the stated aim of a paper.

Key explanatory variables (secondary ions) in the multivariate dataset can be identified which correlate either positively or negatively with the prediction (WCA).

An unsupervised principal component analysis (PCA) was performed to obtain an overview of the multivariate dataset and to identify and exclude outliers that could seriously disturb the supervised models [ 31].