Calculate the contribution of PCA loadings to the selected principal components

Total contribution of a variable is calculated as per ((Cx * Ex) + (Cy * Ey))/(Ex + Ey), where:

• Cx and Cy are the contributions of a variable to principal components x and y

• Ex and Ey are the eigenvalues of principal components x and y

Usage

calculateLoadingsContribution(pca, pcX = 1, pcY = 2)

Source

http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/112-pca-principal-component-analysis-essentials/

Arguments

pca

prcomp object

pcX

Character: name of the X axis of interest from the PCA

pcY

Character: name of the Y axis of interest from the PCA

Value

Data frame containing the correlation between variables and selected principal components and the contribution of variables to the selected principal components (both individual and total contribution)

See also

Other functions to analyse principal components: performPCA(), plotPCA(), plotPCAvariance()

Examples

pca <- performPCA(USArrests)
calculateLoadingsContribution(pca)
#>          Rank     Gene PC1 loading PC2 loading Contribution to PC1 (%)
#> Assault     1  Assault  0.99522128 -0.05876003              99.0465399
#> UrbanPop    2 UrbanPop  0.04633575  0.97685748               0.2147001
#> Rape        3     Rape  0.07515550  0.20071807               0.5648349
#> Murder      4   Murder  0.04170432 -0.04482166               0.1739250
#>          Contribution to PC2 (%) Contribution to PC1 and PC2 (%)
#> Assault                0.3452741                      96.2825574
#> UrbanPop              95.4250536                       2.8809248
#> Rape                   4.0287742                       0.6618374
#> Murder                 0.2008981                       0.1746804