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Editorial
. 2017 Sep;5(17):351.
doi: 10.21037/atm.2017.07.12.

Principal components analysis in clinical studies

Zhongheng Zhang  1 , Adela Castelló  2   3
Affiliations
Editorial

Principal components analysis in clinical studies

Zhongheng Zhang et al. Ann Transl Med. 2017 Sep.

Abstract

In multivariate analysis, independent variables are usually correlated to each other which can introduce multicollinearity in the regression models. One approach to solve this problem is to apply principal components analysis (PCA) over these variables. This method uses orthogonal transformation to represent sets of potentially correlated variables with principal components (PC) that are linearly uncorrelated. PCs are ordered so that the first PC has the largest possible variance and only some components are selected to represent the correlated variables. As a result, the dimension of the variable space is reduced. This tutorial illustrates how to perform PCA in R environment, the example is a simulated dataset in which two PCs are responsible for the majority of the variance in the data. Furthermore, the visualization of PCA is highlighted.

Keywords: Principal component analysis; R; multicollinearity; regression.

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Conflict of interest statement

Conflicts of Interest: The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Schematic illustration of how the principal components analysis works.
Figure 2
Figure 2
Screeplot representing the variances of all principal components.
Figure 3
Figure 3
Graphical display of multivariate data with biplot.
Figure 4
Figure 4
Graphical display of multivariate data with biplot.
Figure 5
Figure 5
Component loadings that characterize the strength and sing of the association of each independent variable (x1–x5) with each principal component (PC1–PC5).
See this image and copyright information in PMC

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