2017
DOI: 10.1201/b21874
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Exploratory Multivariate Analysis by Example Using R

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Cited by 584 publications
(542 citation statements)
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“…We examined genetic population structure with STRUC-TURE version 2.3.4 (Pritchard et al 2000). To further test for genetic structure, we employed principal component analysis (PCA), with FactoMineR (Husson et al 2010) in R. STRUC-TURE and PCA were performed on both the GBS and RNAseq datasets. As our sampling design did not include the purported cytotype contact zone (shaded area in Fig.…”
Section: Demographic Analysesmentioning
confidence: 99%
“…We examined genetic population structure with STRUC-TURE version 2.3.4 (Pritchard et al 2000). To further test for genetic structure, we employed principal component analysis (PCA), with FactoMineR (Husson et al 2010) in R. STRUC-TURE and PCA were performed on both the GBS and RNAseq datasets. As our sampling design did not include the purported cytotype contact zone (shaded area in Fig.…”
Section: Demographic Analysesmentioning
confidence: 99%
“…To assess how traits covaried, we performed a principal component analysis (PCA) on the fourteen quantitative traits using the R package 'FactoMineR' (Husson et al 2010). For each trait, we scaled the data to the unit variance.…”
Section: Trait Analysesmentioning
confidence: 99%
“…62 ] (v.test criterion) also gives an indication of whether the mean of the cluster for that variable is larger (positive sign) or smaller (negative sign) than the overall mean in the dataset. 70 Only significant variables in clusters were considered. Finally, the supplementary categories (i.e.…”
Section: Methodsmentioning
confidence: 99%