principal component analysistime‐varyingWe utilise principal component analysis to determine whether a (small) set of factors can explain cryptocurrency returns and whether this varies over time. We find that a substantial proportion of cryptocurrency return variation is explained by a single principal ...
Principal component analysis demonstrated that the first two principal components (PCs) accounted for 83.5% of the variance in measured variables (PC1: 59.9%, PC2: 23.6%; Fig. 8), and clearly separated soils by fertility management history. Lime rate was closely associated with pH and POXC, ...
4g). We also performed principal component analysis and calculated the Mahalanobis distances of the principal axes for the three bond types36, which are statistically separated in the catch-bond intensity vs Mahalanobis distance plot (Fig. 4h). Interestingly, WC and SC bonds show distinct ...
principal component analysisdiscriminant analysispartial-least squaresA 3-D chemistry-transport model has been applied to the Mexico City metropolitan area to investigate the origin of elevated levels of non-fossil (NF) carbonaceous aerosols observed in this highly urbanized region. High time resolution ...
Upon analyses of 15 plates with urine samples, QC samples clustered closely together in the principal component analysis score plots, confirming a stable UPLC system during the course of analysis with the exception of two plates in the negative mode and one plate in the positive mode, which had...
Third, we used WorldClim climate data60 to calculate the mean values of each of 19 bioclimatic variables for each grid cell, and then we ran a principal component analysis (PCA) of all bioclimatic variables using the prcomp function in the stats package version 4.2.161. Prior to running the...
Once we compute the 12-dimensional metric for each network, Principal Component Analysis (PCA) is used to validate the usefulness of the proposed statistic. In addition, to enabling visual inspection of our data, it can be used to verify if some dimensions of the 12-dimensional metric could ...
we flip the sign for RMSE and MAE and examine negative RMSE and negative MAE. Within each fold, we applied principal component analysis (PCA) to reduce the dimensionality ofZto the 9 PCs that explained ≥1% variance in the data. Additionally, age and sex were controlled by regressing their ...
2.3. Data analysis To avoid collinearity among various factors, the 14 factors that affect treeline elevation were reduced by principal component analysis (PCA), and multiple variables were converted into principal components that could reflect the main information of the original variables. The variable...
To determine whether elevation zonation or lifestyle impose phenotypic constraints, we assessed the breadth of the morphospace of each ecotype via principal component analysis (PCA) and by measuring the skull shape diversity. Here, we consider shape diversity as the variety of forms present in each...