A principal components analysis found that factors positively affecting preference for hedges included neatness, foliage colour (green and grey), presence of flowers and the absence of visible woody stems.
As shown in Tables 1 and 2, 4 to 10 principal components were found useful in our study.
As before, we performed ANOVAs of the first six Principal Components and found significant effects of population, sex, rearing substrates, and body parts for PC 1, as well as a significant population by sex interaction (Table S3).
The principal components are found by maximizing the description of data variance.
Principal components analysis found one component with an eigenvalue >1 that explained 61% of the total variance in the underlying latent construct of neighborhood deprivation.
Investigating the eigenvalues of the first principal components, we found that the first component contained the highest overall data variance while the first and second principal component combined contributed to approximately half of the overall variance.
Principal components analysis found that the overall variation of gene expression was due to the biology of the tumors, and not by cohort/source or microarray platform (Supplemental Fig. 1).
The first two principal components were found to explain ∼70% of the sequence variability across strains and clearly separate Japanese Wistar-derived strains, SD-derived strains, and the remaining rat strains .
We plotted the upper/lower lobe pairs according to expression of the first three principal components and found that clusters were not determined by lobe, but rather by the patient (intraclass correlation coefficient = 0.474, p-value = 0.02 [for the first principal component]).
The first ten principal components were found to account for 86% of the total SNP genotype variation, while the first four principal components provided good separation among the major self-reported 'ethnicities' (white, black, Hispanic, Asian/Pacific Islander, northern versus southern European ancestry).
