ObjectiveTo provide method references for data visualization of multiple linear regression analysis.MethodsAfter 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.ResultsThe multiple linear regression analysis implemented by R Studio software had better visualization functions and easier operation than traditional R language software.ConclusionsR Studio software has good application value in realizing multiple linear regression analysis data visualization.
ObjectiveTo combine specific examples and R Studio language code, to apply the Bayesian quantile regression method in the analysis of clinical medicine data, and show the advantages of Bayesian quantile regression method, so as to provide references for improving the accuracy of medical research. Methods The clinical data of 250 patients with knee osteoarthritis from the capital special research on the application of clinical characteristics project were used. A Bayesian quantile regression model based on data set was constructed to explore the relationship between the level of serum IgG and the age of the patients. Results The Monte Carlo algorithm converge can judge the efficiency of parameter estimation based on Gibbs sampling which was used to draw samples from the posterior distribution of parameters in Bayesian quantile regression. By generating the parameter into the regression formula, we can obtain the regression under different quantiles: Y1=−6.022 063 47+2.026 913 73X−0.015 077 69X2……Y5=24.610 542 414−0.395 059 497X+0.004 205 064X2. It can be found that the serum level of IgG was obviously increased with age. Conclusion Bayesian quantile regression parameter estimation results are accurate and highly credible, and reliable parameter information can be obtained even under small sample conditions. It has great advantages in the research of clinical medicine data and has certain promotional value.
Incidence rate is a common effect measure. The incidence rate ratio refers to the ratio of two different incidence rates. It is used to compare the difference in the number of cases per unit person-time between two groups. RevMan software can not perform a meta-analysis with the incidence rate ratio as the effect size at present. A set of simulation data was used to demonstrate a meta-analysis process with the incidence rate ratio as the effect size by using the meta package of R Studio software in this article.