R语言线性回归数据分析案例可视化报告 (附代码数据)

R语言线性回归数据分析案例可视化报告从源下载数据集。pima<-read.table("/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data",header=F,sep=",")清理数据2.a放入数据列colnames(pima)<-c("npreg","glucose","bp","triceps","insulin","bmi","diabetes","age","class")head(pima)##npregglucosebptricepsinsulinbmidiabetesageclass##161487235033.60.627501##21856629026.60.351310##38183640023.30.672321##418966239428.10.167210##50137403516843.12.288331##65116740025.60.201300head(pima)##npregglucosebptricepsinsulinbmidiabetesageclass##161487235033.60.627501##21856629026.60.351310##38183640023.30.672321##418966239428.10.167210##50137403516843.12.288331##65116740025.60.201300pairs(pima,gap=0.5,col='navyblue')pimalm<-lm(class~npreg+glucose+bp+triceps+insulin+bmi+diabetes+age,data=pima)summary(pimalm)####Call:##lm(formula=class~npreg+glucose+bp+triceps+insulin+##bmi+diabetes+age,data=pima)####Residuals:##Min1QMedian3QMax##-1.01348-0.29513-0.095410.321121.24160####Coefficients:##EstimateStd.ErrortvaluePr(>|t|)##(Intercept)-0.85389430.0854850-9.989<2e-16***##npreg0.02059190.00513004.0146.56e-05***##glucose0.00592030.000515111.493<2e-16***##bp-0.00233190.0008116-2.8730.00418**##triceps0.00015450.00111220.1390.88954##insulin-0.00018050.0001498-1.2050.22857##bmi0.01324400.00208786.3443.85e-10***##diabetes0.14723740.04505393.2680.00113**##age0.00262140.00154861.6930.09092.##---##Signif.codes:0'***'0.001'**'0.01'*'0.05'.'0.1''1####Residualstandarderror:0.4002on759degreesoffreedom##MultipleR-squared:0.3033,AdjustedR-squared:0.2959##F-statistic:41.29on8and759DF,p-value:<2.2e-16去除大p值的变量（p值>0.005）pimalm<-update(pimalm,.~.-triceps,data=pima)summary(pimalm)####Call:##lm(formula=class~npreg+glucose+bp+insulin+bmi+diabetes+##age,data=pima)####Residuals:##Min1QMedian3QMax##-1.01707-0.29614-0.096560.320731.24183####Coefficients:##EstimateStd.ErrortvaluePr(>|t|)##(Intercept)-0.85379060.0854265-9.994<2e-16***##npreg0.02059390.00512664.0176.48e-05***##glucose0.00590920.000508611.619<2e-16***##bp-0.00231520.0008022-2.8860.00401**##insulin-0.00017210.0001370-1.2570.20929##bmi0.01333820.00197336.7592.76e-11***##diabetes0.14788350.04478433.3020.00100**##age0.00259910.00153931.6880.09173.##---##Signif.codes:0'***'0.001'**'0.01'*'0.05'.'0.1''1####Residualstandarderror:0.4on760degreesoffreedom##MultipleR-squared:0.3032,AdjustedR-squared:0.2968##F-statistic:47.25on7and760DF,p-value:<2.2e-16Removevariables(insulin,age)withlargepvalue(pvalue>0.005)Afterthevariablesaredropped,theR-squaredvalueremainaboutthesame.Thissuggeststhevariablesdroppeddonothavemucheffectonthemodel.pimalm<-update(pimalm,.~.-insulin,data=pima)pimalm<-update(pimalm,.~.-age,data=pima)summary(pimalm)####Call:##lm(formula=class~npreg+glucose+bp+bmi+diabetes,data=pima)####Residuals:##Min1QMedian3QMax##-1.12594-0.29476-0.098440.315681.25963####Coefficients:##EstimateStd.ErrortvaluePr(>|t|)##(Intercept)-0.79663690.0815821-9.765<2e-16***##npreg0.02580080.00437415.8985.51e-09***##glucose0.00589870.000472612.482<2e-16***##bp-0.00209110.0007896-2.6480.00826**##bmi0.01282480.00196136.5391.14e-10***##diabetes0.14299090.04443583.2180.00135**##---##Signif.codes:0'***'0.001'**'0.01'*'0.05'.'0.1''1####Residualstandarderror:0.4007on762degreesoffreedom##MultipleR-squared:0.2987,AdjustedR-squared:0.2941##F-statistic:64.92on5and762DF,p-value:<2.2e-16Residualanalysisshowsalmoststraightlinewithdistributionaroundzero.Duetothispattern,thismodelisnotasrobust.plot(fitted(pimalm),resid(pimalm))qqnorm(resid(pimalm),col="blue")qqline(resid(pimalm),col="red")Theseconddatasetwithmuchsimplervariables.Althoughintuitivelythevariablesbotheffecttheoutput,theamountofeffectbyeachvariableisinteresting.Thisdatasetwasexaminedtohaveabettersenseofhowmultivariateregressionwillperform.library(DAAG)##Warning:package'DAAG'wasbuiltunderRversion3.3.3##Loadingrequiredpackage:latticedata(allbacks)plot(allbacks,gap=0.5,col='red')allbacks.lm<-lm(weight~volume+area,data=allbacks)summary(allbacks.lm)####Call:##lm(formula=weight~volume+area,data=allbacks)####Residuals:##Min1QMedian3QMax##-104.06-30.02-15.4616.76212.