R语言菜鸟练习笔记4.docx
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R语言菜鸟练习笔记4
R语言笔记
题目1:
根据MEdata数据集的拟合结果,
预测当GDP依次为6500、7500、8500、10000、12000、15000时的TAX值
并绘制(预测TAX~GDP)散点图
代码:
>abc<-read.csv("D:
/R/MEdata.csv",header=T)
>attach(abc)
>LM<-lm(TAX~GDP)
>gdp<-c(6500,7500,8500,10000,12000,15000)
>tax<-predict(LM,data.frame(GDP=gdp))
>tax
1234
1188.3331375.4631562.5921843.286
56
2217.5452778.933
>plot(tax~gdp)
截图:
题目2:
提取1993-2013年的TAX和GDP,与刚才的TAX~GDP预测值合并
绘制合并后的(TAX~GDP)散点图
代码:
>t<-abc[16:
36,"TAX"]
>tax<-c(t,tax)
>g<-abc[16:
36,"GDP"]
>gdp<-c(g,gdp)
>plot(tax~gdp)
运行结果截图
题目3:
将DataC数据集,使用各种曲线方程进行拟合
找出其最符合哪种曲线类型,并求出其拟合方程
分析其拟合可信度
决定系数、校正决定系数
代码:
>abc<-read.csv("C:
/Users/user/Desktop/DataA-C/DataC.csv",header=T)
>data<-abc[1:
99,]
>x<-c(1:
99)
>lines(x,fitted(lmzb))
>lmpf<-lm(data~exp(x))
>lines(x,fitted(lmpf))
>lmpf<-lm(data~exp(x))
>lines(x,fitted(lmpf))
>lmlg<-lm(data~log(x))
>lines(x,fitted(lmlg))
>lm1<-lm(data~x^2)
>lines(x,fitted(lm1))
>summary(lm1)
Call:
lm(formula=data~x^2)
Residuals:
Min1QMedian3QMax
-4.1099-1.9491-0.20971.75606.6147
Coefficients:
EstimateStd.ErrortvaluePr(>|t|)
(Intercept)-5.6070230.519329-10.80<2e-16
x0.3142770.00901834.85<2e-16
(Intercept)***
x***
---
Signif.codes:
0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1
Residualstandarderror:
2.564on97degreesoffreedom
MultipleR-squared:
0.926,AdjustedR-squared:
0.9253
F-statistic:
1215on1and97DF,p-value:
<2.2e-16
>summary(lmzb)
Call:
lm(formula=data~x)
Residuals:
Min1QMedian3QMax
-4.1099-1.9491-0.20971.75606.6147
Coefficients:
EstimateStd.ErrortvaluePr(>|t|)
(Intercept)-5.6070230.519329-10.80<2e-16
x0.3142770.00901834.85<2e-16
(Intercept)***
x***
---
Signif.codes:
0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1
Residualstandarderror:
2.564on97degreesoffreedom
MultipleR-squared:
0.926,AdjustedR-squared:
0.9253
F-statistic:
1215on1and97DF,p-value:
<2.2e-16
>lm2<-lm(data~x^3)
>lines(x,fitted(lm2))
>summary(lm2)
Call:
lm(formula=data~x^3)
Residuals:
Min1QMedian3QMax
-4.1099-1.9491-0.20971.75606.6147
Coefficients:
EstimateStd.ErrortvaluePr(>|t|)
(Intercept)-5.6070230.519329-10.80<2e-16
x0.3142770.00901834.85<2e-16
(Intercept)***
x***
---
Signif.codes:
0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1
Residualstandarderror:
2.564on97degreesoffreedom
MultipleR-squared:
0.926,AdjustedR-squared:
0.9253
F-statistic:
1215on1and97DF,p-value:
<2.2e-16
>lm3<-lm(data~x^4)
>lines(x,fitted(lm3))
>summary(lm3)
Call:
lm(formula=data~x^4)
Residuals:
Min1QMedian3QMax
-4.1099-1.9491-0.20971.75606.6147
Coefficients:
EstimateStd.ErrortvaluePr(>|t|)
(Intercept)-5.6070230.519329-10.80<2e-16
x0.3142770.00901834.85<2e-16
(Intercept)***
x***
---
Signif.codes:
0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1
Residualstandarderror:
2.564on97degreesoffreedom
MultipleR-squared:
0.926,AdjustedR-squared:
0.9253
F-statistic:
1215on1and97DF,p-value:
<2.2e-16
>lm4<-lm(data~(x^2+x)
+)
>line(x~fitted(lm4))
Call:
line(x~fitted(lm4))
Coefficients:
[1]17.3463.231
>lines(x~fitted(lm4))
>summary(lm4)
Call:
lm(formula=data~(x^2+x))
Residuals:
Min1QMedian3QMax
-4.1099-1.9491-0.20971.75606.6147
Coefficients:
EstimateStd.ErrortvaluePr(>|t|)
(Intercept)-5.6070230.519329-10.80<2e-16
x0.3142770.00901834.85<2e-16
(Intercept)***
x***
---
Signif.codes:
0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1
Residualstandarderror:
2.564on97degreesoffreedom
MultipleR-squared:
0.926,AdjustedR-squared:
0.9253
F-statistic:
1215on1and97DF,p-value:
<2.2e-16
>lm5<-lm(data~(I(x^2)))
>summary(lm5)
Call:
lm(formula=data~(I(x^2)))
Residuals:
Min1QMedian3QMax
-1.9939-0.5952-0.17670.50522.8117
Coefficients:
EstimateStd.ErrortvaluePr(>|t|)
(Intercept)-0.3321010.146471-2.2670.0256
I(x^2)0.0031470.00003395.376<2e-16
(Intercept)*
I(x^2)***
---
Signif.codes:
0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1
Residualstandarderror:
0.9685on97degreesoffreedom
MultipleR-squared:
0.9894,AdjustedR-squared:
0.9893
F-statistic:
9097on1and97DF,p-value:
<2.2e-16
>lm6<-lm(data~x^3)
>summary(lm6)
Call:
lm(formula=data~x^3)
Residuals:
Min1QMedian3QMax
-4.1099-1.9491-0.20971.75606.6147
Coefficients:
EstimateStd.ErrortvaluePr(>|t|)
(Intercept)-5.6070230.519329-10.80<2e-16
x0.3142770.00901834.85<2e-16
(Intercept)***
x***
---
Signif.codes:
0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1
Residualstandarderror:
2.564on97degreesoffreedom
MultipleR-squared:
0.926,AdjustedR-squared:
0.9253
F-statistic:
1215on1and97DF,p-value:
<2.2e-16
>lm7<-lm(data~I(x^3))
>lines(x,fitted(lm7))
>summary(lm7)
Call:
lm(formula=data~I(x^3))
Residuals:
Min1QMedian3QMax
-4.2920-1.35950.01251.12994.6468
Coefficients:
EstimateStd.ErrortvaluePr(>|t|)
(Intercept)1.993e+002.440e-018.171.17e-12
I(x^3)3.278e-056.537e-0750.15<2e-16
(Intercept)***
I(x^3)***
---
Signif.codes:
0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1
Residualstandarderror:
1.817on97degreesoffreedom
MultipleR-squared:
0.9629,AdjustedR-squared:
0.9625
F-statistic:
2515on1and97DF,p-value:
<2.2e-16
>lm8(data~I(x+x^2))
Error:
couldnotfindfunction"lm8"
>lm8<-lm(data~I(x+x^2))
>lines(x,fitted(lm8))
>summary(lm8)
Call:
lm(formula=data~I(x+x^2))
Residuals:
Min1QMedian3QMax
-1.9444-0.5931-0.14100.52662.8217
Coefficients:
EstimateStd.Errortvalue
(Intercept)-3.907e-011.472e-01-2.655
I(x+x^2)3.118e-033.274e-0595.227
Pr(>|t|)
(Intercept)0.00927**
I(x+x^2)<2e-16***
---
Signif.codes:
0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1
Residualstandarderror:
0.97on97degreesoffreedom
MultipleR-squared:
0.9894,AdjustedR-squared:
0.9893
F-statistic:
9068on1and97DF,p-value:
<2.2e-16
截图
题目4:
针对MEdata数据集
分析TAX与EXP、IE、RS、COM、INV、DEP中的哪些变量更为相关?
代码:
>xyz<-read.csv("C:
/Users/user/Desktop/MEdata.csv",header=T)
>xyz
XTAXGDPEXPIE
119785.192836.05611.22093.550
219795.378240.92612.81794.546
319805.717045.92912.28835.700
419816.298950.08811.38417.353
519827.000255.90012.29987.713
619837.755962.16214.09528.601
719849.473573.62717.010212.010
8198520.407990.76720.042520.667
9198620.9073105.08522.049125.804
10198721.4036122.77422.621830.842
11198823.9047153.88624.912138.218
12198927.2740173.11328.237841.560
13199028.2186193.47830.835955.601
14199129.9017225.77433.866272.258
15199232.9691275.65237.422091.196
16199342.5530369.38146.4230112.710
17199451.2688502.17457.9262203.819
18199560.3804632.16968.2372234.999
19199669.0982741.63679.3755241.338
20199782.3404816.58592.3356269.672
21199892.6280865.316107.9818268.497
221999106.8258911.250131.8767298.962
232000125.8151987.490158.8650392.732
242001153.01381090.280189.0258421.836
252002176.36451204.756220.5315513.782
262003200.17311366.134246.4995704.835
272004241.65681609.566284.8689955.391
282005287.78541874.234339.30281169.218
292006348.04352227.125404.22731409.740
302007456.21972665.992497.81351668.637
312008542.23793159.746625.92661799.215
322009595.21593487.751762.99931506.481
332010732.10794028.165898.74162017.222
342011897.38394726.1921092.47792364.020
3520121006.14285293.9921259.52972441.602
3620131105.30705866.7301402.12102582.529
RSCOMINVDEP
115.58617.59108.0082.1060
218.00020.11508.5652.8100
321.40023.31209.1093.9580
423.50026.27909.6105.2370
525.70029.029012.3046.7540
628.49432.311014.3018.9250
733.76437.420018.32912.1470
843.05046.874025.43216.2260
949.50053.021031.20622.3850
1058.20061.261037.91730.8140
1174.40078.681047.53838.2220
1281.01488.126044.10451.9640
1383.00194.509045.17071.1960
1494.156107.306055.94592.4490
15109.937130.001080.801117.5730
16142.704164.1210130.723152.0350
17186.229218.4420170.421215.1880
18236.138283.6970200.193296.6230
19283.602339.5590229.135385.2080
20312.529369.2150249.411462.7980
21333.781392.2930284.062534.0750
22356.479419.2040298.547596.2180
23391.057458.5460329.177643.3240
24430.554494.3590372.135737.6240
25481.359530.5660434.999869.1065
26525.163576.4980555.6661036.1731
27595.010652.1850704.7741195.5539
28671.766729.5870887.7361410.5099
29791.452825.75451099.9821615.8730
30935.716963.32501373.2391725.3419
311148.3011116.70401728.2842178.8535
321326.7841235.84622245.9882607.7166
331569.9841407.58652516.8383033.0249
341839.1861689.56633114.8513436.3589
352103.0701905.84603746.9473995.5104
362378.0992121.87504470.7444607.8504
>attach(xyz)
>lm1<-lm(TAX~GDP)
>summary(lm1)
Call:
lm(formula=TAX~GDP)
Residuals:
Min1QMedian3QMax
-42.459-29.5557.86124.98243.489
Coefficients:
EstimateStd.Errortvalue
(Intercept)-28.0072966.274253-4.464
GDP0.1871290.00306761.015
Pr(>|t|)
(Intercept)8.42e-05***
GDP<2e-16***
---
Signif.codes:
0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1
Residualstandarderror:
29.35on34degreesoffreedom
MultipleR-squared:
0.9909,AdjustedR-squared:
0.9907
F-statistic:
3723on1and34DF,p-value:
<2.2e-16
>lm2<-lm(TAX~EXP)
>summary(lm2)
Call:
lm(formula=TAX~EXP)
Residuals:
Min1QMedian3QMax
-28.316-7.063-1.0191.28951.037
Coefficients:
EstimateStd.ErrortvaluePr(>|t|)
(Intercept)4.1825862.8122521.4870.146
EXP0.8055230.006217129.574<2e-16