Sentence examples for direction of variability from inspiring English sources

Therefore, it is necessary to examine spatial and temporal variability simultaneously in order to understand the direction of variability evolving in space and time.

The new coordinates are the principal components such that the first PC represents the direction of greatest variability, the second greatest variance lies on the second PC and so on.

Thus, the first eigenvector (linear combination) identifies the direction of largest variability, the second the one of next largest variability, subject to the constraint of being orthogonal to the first, etc (see Figure 1).

S4VDPCA can consistently select the truly relevant variables contributing to a sparse PC while also consistently estimate the direction of maximal variability.

However, it is known that PCA may not consistently estimate the true direction of maximal variability in high-dimensional, low sample size settings, which are typical for molecular data.

We also recommend that when interpreting the results of such investigations to consider confounding, other sources of bias e.g., publication, misclassification, dilution, selection; [ 20, 32], magnitude and direction of effect, variability in effect [ 1, 20], and thinking through the plausibility of causal relationships for potential influential clinical variables [ 41].

The new axes acquired will represent the directions with maximum variability where PC1 indicates the direction of the highest variability followed by consecutive PCs with diminishing variability orthogonal to the previous PC or PC plane [ 16].

To determine the shape patterns of the population, the shape-space is rotated in such a way that its main axes are aligned to the directions of major variability of the population.

The different trends of variation hidden in the initial multidimensional space are evidenced since the new orthogonal axes of the projected space (the two first principal components) are derived from the directions of larger variability [ 17].

Using the ODM, the wind speed data is observed in a new coordinate system, in which each axis is aligned along the direction of maximum joint variability.

To visualize the direction of the total variability in our samples without taking the class labels into consideration, we used an unsupervised approach (Principal Component Analysis, PCA) and observed that EV and MYC2-VIGS samples were separated to two clusters by PCA, suggesting genotype-specific differences at the level of metabolites.