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00925njm 22002777a 4500 dc Zhang, Zhongheng author Castelló Pastor, Adela author 2017-09 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. Ann Transl Med. 2017 Sep;5(17):351. 10.21037/atm.2017.07.12 2305-5839 Annals of translational medicine 28936445 http://hdl.handle.net/20.500.12105/8629 Principal component analysis R Multicollinearity Regression Principal components analysis in clinical studies