More importantly, all subsequent analyses in the paper (including principal variable analysis and principal component analysis) rely on the assumption of normality of the data, and utilize standard total variance calculations that are based on Pearson-like products of non-rank-transformed data.
The SIMCA14.1 software package (Umetrics, Umea, Sweden) was used for multivariate variable pattern recognition analysis: principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least-squares discriminant analysis (OPLS-DA).
We additionally subjected predictor variables to factor analysis (principal components) to explore between-variable colinearity prior to building a multiple logistic regression model as described by Hosmer and Lemeshow.
Simultaneous analysis of all observed variables: multivariate data analysis, principal component analysis, correlations analysis, factorial analysis and clustering Multivariate comparisons of the different variables: hypothesis testing, multiple ways ANOVA, MANOVA, and others to determinate if the groups differ relative to the different questions: specific GMO effect or diet effect per se.
Simultaneous analysis of all observed variables: multivariate data analysis, principal component analysis, correlations analysis, factorial analysis and clustering.
Based on these considerations, and owing to your clear and constructive feedback, we now include an analysis of overall involvement of different variables in linear correlations with all other variables in the set (the method known as Principal Variable Analysis (Mccabe, 1984) before moving to the factor analysis.
To make sure that none of the variables were redundant (brought no new information to the set), or too noisy (having no interactions with other variables in the set), we ran the so-called Principal Variable Analysis, quantifying the total amount of variance in the dataset explained by each variable (Mccabe, 1984).
Correlations between the Brazilian Cerrado soils' regions and the physicochemical soil variables were subjected to statistical analysis (principal component analysis or PCA) using the Unscrambler 9.7 software (CAMO, Oslo, Norway).
We utilized four data decomposition methods – independent surrogate variable analysis (ISVA), surrogate variable analysis (SVA), principal component analysis (PCA) and PEER [ 13, 22] – to account for unknown covariates.
We ran the Principal Variables Analysis in R — package 'subselect' (Mccabe, 1984; Cadima and Jolliffe, 2001; Cadima et al., 2004) — using the correlation matrix of original non-imputed data, which was computed on all available pairwise observations for each pair of variables.
