LI Duoduo 1 , YU Xing 1 , HAN Sheng 2,3 , ZHU He 2,3 , YUAN Yi 1 , SHEN Jie 4 , LIN Jingfeng 5 , LI Xia 1 , GAN Yena 1 , LIU Jianping 6
  • 1. Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, P.R.China;
  • 2. School of Pharmaceutical Science, Peking University, Beijing 100191, P.R.China;
  • 3. International Research Center for Medicinal Administration, Peking University, Beijing 100191, P.R.China;
  • 4. iHealth Labs Inc, Shanghai 200235, P.R.China;
  • 5. Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, P.R.China;
  • 6. Centre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, P.R.China;
YU Xing, Email: yuxing34@sina.com; LIU Jianping, Email: liujp@bucm.edu.cn
R language; Data visualization; R Studio; Medicine; Multiple linear regression analysis (MLRA)
10.7507/1672-2531.202008172
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Objective To provide method references for data visualization of multiple linear regression analysis.Methods After importing data to R Studio, this paper conducted general descriptive statistics analysis, then constructed a linear model between independent variables and the target. After checking independence of observations, the normality of the target, and the linearity between variables, this paper estimated coefficients of independent variables, dealt with multicollinearity, tested significance of estimates and performed residual analysis to guarantee that the regression met its assumptions, and eventually used the fitted model for prediction.Results The multiple linear regression analysis implemented by R Studio software had better visualization functions and easier operation than traditional R language software.Conclusions R Studio software has good application value in realizing multiple linear regression analysis data visualization.

Citation： LI Duoduo, YU Xing, HAN Sheng, ZHU He, YUAN Yi, SHEN Jie, LIN Jingfeng, LI Xia, GAN Yena, LIU Jianping. Data visualization of multiple linear regression analysis practiced by R Studio software. Chinese Journal of Evidence-Based Medicine, 2021, 21(4): 482-490. doi: 10.7507/1672-2531.202008172 Copy

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