2026-06-30T03:03:19Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/86292024-09-27T16:49:28Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19608
Repisalud
author
Zhang, Zhongheng
author
Castelló Pastor, Adela
2019-11-21T10:24:32Z
2019-11-21T10:24:32Z
2017-09
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
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.
eng
Principal component analysis
R
Multicollinearity
Regression
Principal components analysis in clinical studies
journal article
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URL
https://repisalud.isciii.es/bitstreams/926a82c6-36b8-45d1-a53e-8ed8bcd61d9b/download
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PrincipalComponentsAnalysisClinical_2017.pdf
URL
https://repisalud.isciii.es/bitstreams/4e617956-ad32-4d07-8b30-4d2b123b643a/download
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PrincipalComponentsAnalysisClinical_2017.pdf.txt