The phrase "all the principal components" is correct and usable in written English.
It can be used in contexts related to data analysis, statistics, or machine learning, particularly when discussing the components derived from techniques like Principal Component Analysis (PCA).
Example: "In our analysis, we examined all the principal components to understand the underlying structure of the data."
Alternatives: "all the main components" or "all the key components".
Eigen value indicated how well each of the identified factors fit the data from all the geomorphic parameters on all the principal components.
In this approach, the proportion of overall variation explained by each component is treated as the weight to combine all the principal components in order to form a multi-response performance index (MPI).
Indeed, extreme groups are those where the mean values of all the principal components (the centroids) have opposite signs, and thus attitude to food technology should be better defined.
The weighted principal component (WPC -based procedure (Su and Tong 1997; Liao 2006) for optimization of multi-response processes makes use of all the principal components irrespective of the eigenvalues so that the overall variation in all the responses can be completely explained.
This has allowed us to label all the principal components rather than a few.
The method is described in detail in the paper, but briefly we defined regions of interest (ROIs) in the videos corresponding to the body parts and measured the pixel changes in these ROIs for all the principal components, and decided based on a threshold what body parts were involved in the principal component.
When the initial genes are transformed by either PCA or ICA, all of the principal components or independent components are kept to preserve the discriminatory information.
The variance components are averaged across all of the principal components using the corresponding eigenvalues as weights, and the magnitude of each source of variation is presented as a proportion of total variance.
To perform the present analysis with all genes and in all samples, the initial step was to plot scatter plots of all combinations of the principal components (Figure 6A, B).
We have improved our classification of principal components using a semi-automated labelling algorithm, now labelling all 1000 of the principal components.
In fraction FC111, qualitatively representative of all PC extracts, the principal components were anthocyanins, peaks 21, 24 29, 31, 33 (40%), flavonols, peaks 30 32, 34 44 (25%), iridoids, peaks 2a,b, 3, 16b (22%), and the conjugated glycosides of simple phenolic acids, peaks 4 15, 16a, 17 19 (9%), based on integrated peak areas recorded at 254 nm measured against total absorbance.
