2008
DOI: 10.18637/jss.v023.i12
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CCA: AnRPackage to Extend Canonical Correlation Analysis

Abstract: Canonical correlations analysis (CCA) is an exploratory statistical method to highlight correlations between two data sets acquired on the same experimental units. The cancor() function in R (R Development Core Team 2007) performs the core of computations but further work was required to provide the user with additional tools to facilitate the interpretation of the results. We implemented an R package, CCA, freely available from the Comprehensive R Archive Network (CRAN, http://CRAN.R-project.org/), to develop… Show more

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Cited by 312 publications
(310 citation statements)
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“…We used the CCA package (González and Déjean 2012) for the statistical software R (R Core Team 2015) to apply linear discriminant analysis and extract the major axis along the multivariate phenotypic dimensions discriminating between the sexes (i.e., best describing maleness vs. femaleness) in each of the four composite traits. To derive the discriminant function for the adult life-history syndrome, we mean-centered and unit-variance standardized the four traits (body mass, life span, locomotor activity, and metabolic rate), measuring them on a common scale, ascertaining that each trait could contribute equally to the extracted scores.…”
Section: Genetic Variance and Sexual Dimorphism In Compositementioning
confidence: 99%
“…We used the CCA package (González and Déjean 2012) for the statistical software R (R Core Team 2015) to apply linear discriminant analysis and extract the major axis along the multivariate phenotypic dimensions discriminating between the sexes (i.e., best describing maleness vs. femaleness) in each of the four composite traits. To derive the discriminant function for the adult life-history syndrome, we mean-centered and unit-variance standardized the four traits (body mass, life span, locomotor activity, and metabolic rate), measuring them on a common scale, ascertaining that each trait could contribute equally to the extracted scores.…”
Section: Genetic Variance and Sexual Dimorphism In Compositementioning
confidence: 99%
“…HLPC-DAD data were used for the statistical analyses. We carried out statistical tests using the software R together with the packages vegan [21] and CCA [22]. The variables latitude (UTM), longitude (UTM), and altitude (m) were employed as descriptive variables for the formation of groups of samples collected at different locations and altitudes.…”
Section: Chemical Variability Statistical Evaluationmentioning
confidence: 99%
“…Correlations were considered significant at a Po0.05 baseline and to be nearly significant at 0.05oPo0.10. GeoChip and PhyloChip datasets were related to each other using regularized canonical correlation analyses (RCCorA) in the R package (González et al, 2008). The strongest associations in the resulting graphs were identified by calculating Bray-Curtis distance and Pearson's linear correlation between functional genes and taxa.…”
Section: Statistical Analysesmentioning
confidence: 99%