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| ZKR | CPIR | GYLJ | GDTZ | FDC | SHXFLJ | JCK | KCKLJ | HYL | HYLLJ | GLHY | GLHYLJ | CZ | CZLJ | M2 | M1 | PMI_R | | 26.8 | 2.3 | 5.4 | 10.2 | 3 | 10.2 | -20.8 | -17.3 | -0.5 | 0.7 | -1.8 | 1.3 | 7 | 6.3 | 13.3 | 17.4 | -1 | | -8 | 1.8 | 6.2 | 11.4 | 4.3 | 10.4 | -14.3 | -14.5 | 1.5 | 1.5 | 3 | 3 | 5.8 | 5.8 | 14 | 18.6 | -0.6 | | 7 | 1.4 | 6.1 | 10 | 1 | 10.7 | -4.1 | -8 | 5.4 | 4.4 | 7.4 | 6.4 | 9.2 | 8.4 | 13.3 | 15.2 | -0.3 | | -1 | 1.4 | 6.1 | 10.2 | 1.3 | 10.6 | -7.6 | -8.5 | 5.4 | 4.3 | 7.5 | 6.2 | 11.4 | 8 | 13.7 | 15.7 | -0.4 | | 10 | 1.4 | 6.1 | 10.2 | 2 | 10.6 | -12.1 | -8.6 | 4.6 | 4.2 | 6.5 | 6.1 | 8.7 | 7.7 | 13.5 | 14 | -0.2 | | -15 | 1.4 | 6.2 | 10.3 | 2.6 | 10.5 | -11.4 | -8.1 | 3.8 | 4.1 | 5.6 | 6 | 9.4 | 7.6 | 13.1 | 11.4 | -0.2 | | -28 | 1.4 | 6.3 | 10.9 | 3.5 | 10.5 | -9.1 | -7.5 | 3.7 | 4.2 | 5.8 | 6.1 | 6.2 | 7.4 | 13.3 | 9.3 | -0.3 | | -19 | 1.3 | 6.3 | 11.2 | 4.3 | 10.4 | -8.2 | -7.2 | 4.2 | 4.2 | 5.7 | 6.1 | 12.5 | 7.5 | 13.3 | 6.6 | 0 | | -31 | 1.3 | 6.3 | 11.4 | 4.6 | 10.4 | -1.2 | -6.9 | 3.5 | 4.2 | 5.1 | 6.2 | 13.9 | 6.6 | 11.8 | 4.3 | 0.2 | | -41 | 1.3 | 6.2 | 11.4 | 5.1 | 10.4 | -9.3 | -8 | 5.6 | 4.4 | 7.7 | 6.4 | 5 | 5 | 10.8 | 4.7 | 0.2 | | -34 | 1.3 | 6.2 | 12 | 6 | 10.4 | -11.1 | -7.6 | 3 | 4.1 | 5.3 | 6.1 | 8.2 | 5.1 | 10.1 | 3.7 | 0.1 | | -24 | 1.2 | 6.4 | 13.5 | 8.5 | 10.6 | -13.8 | -6.3 | -2.5 | 4.5 | -2.2 | 6.4 | 5.8 | 3.9 | 11.6 | 2.9 | 0.1 | | -60 | 1.1 | 6.8 | 13.9 | 10.4 | 10.7 | 10.8 | -2.3 | 11.2 | 9.2 | 17.2 | 12.5 | 0.3 | 3.2 | 12.5 | 5.6 | -0.1 | | -52 | 0.8 | 6.1 | 11.2 | 2 | 10.4 | -10.9 | -10.9 | 7.9 | 7.9 | 9.9 | 9.9 | 5 | 5 | 10.8 | 10.6 | -0.2 | | -29.5 | 2 | 8.3 | 15.7 | 10.5 | 12 | 4 | 3.4 | 5.6 | 7.1 | 6.4 | 8.7 | 8.2 | 8.6 | 12.2 | 3.2 | 0.1 | | -18.3 | 2 | 8.3 | 15.8 | 11.9 | 12 | -0.5 | 3.4 | 7.1 | 7.3 | 8.6 | 8.8 | 9.1 | 8.3 | 12.3 | 3.2 | 0.3 | | -15.3 | 2.1 | 8.4 | 15.9 | 12.4 | 12 | 8.4 | 3.8 | 4.8 | 7.3 | 5.7 | 8.8 | 9.4 | 8.2 | 12.6 | 3.2 | 0.8 | | -15 | 2.1 | 8.5 | 16.1 | 12.5 | 12 | 11.3 | 3.3 | 9 | 7.7 | 10.9 | 9.2 | 6.3 | 8.1 | 12.9 | 4.8 | 1.1 | | -6.6 | 2.2 | 8.5 | 16.5 | 13.2 | 12.1 | 4 | 2.3 | 8.1 | 7.5 | 9 | 9 | 6.1 | 8.3 | 12.8 | 5.7 | 1.1 | | 3.6 | 2.3 | 8.8 | 17 | 13.7 | 12.1 | 6.9 | 2 | 7.7 | 7.5 | 9.1 | 9.1 | 6.9 | 8.5 | 13.5 | 6.7 | 1.7 | | -15.1 | 2.3 | 8.8 | 17.3 | 14.1 | 12.1 | 6.4 | 1.2 | 9.4 | 7.5 | 11.1 | 9.2 | 8.8 | 8.8 | 14.7 | 8.9 | 1 | | -4.3 | 2.3 | 8.7 | 17.2 | 14.7 | 12.1 | 3 | 0.2 | 6 | 6.8 | 7.6 | 8.3 | 7.2 | 8.8 | 13.4 | 5.7 | 0.8 | | 7.9 | 2.2 | 8.7 | 17.3 | 16.4 | 12 | 0.8 | -0.5 | 6 | 7 | 7.4 | 8.5 | 9.2 | 9.3 | 13.2 | 5.5 | 0.4 | | 12.7 | 2.3 | 8.7 | 17.6 | 16.8 | 12 | -9 | -1 | 5.3 | 7.4 | 6.6 | 9 | 5.2 | 9.3 | 12.1 | 5.4 | 0.3 | | 38.2 | 2.2 | 8.6 | 17.9 | 19.3 | 11.8 | -4.8 | 3.8 | 14.7 | 8.8 | 19.6 | 10.7 | 8.2 | 11.1 | 13.3 | 6.9 | 0.2 | | 19 | 2.5 | 8.7 | 16.1 | 12.5 | 12.1 | 10.3 | 10.3 | 5.4 | 5.4 | 6.4 | 6.4 | 13 | 13 | 13.2 | 1.2 | 0.5 | | 60.5 | 2.6 | 9.7 | 19.6 | 19.8 | 13.1 | 6.2 | 7.6 | 10.9 | 10 | 13.3 | 11.3 | 8.2 | 10.1 | 13.6 | 9.3 | 1 | | 35.3 | 2.6 | 9.7 | 19.9 | 19.5 | 13 | 9.3 | 7.7 | 10.9 | 10 | 12.1 | 11.3 | 15.9 | 9.9 | 14.2 | 9.4 | 1.4 | | 38.8 | 2.6 | 9.7 | 20.1 | 19.2 | 13 | 6.5 | 7.6 | 11.3 | 9.9 | 12.3 | 11.2 | 16.2 | 9.4 | 14.3 | 8.9 | 1.4 | | 49.6 | 2.5 | 9.6 | 20.2 | 19.7 | 12.9 | 3.3 | 7.7 | 10.8 | 9.8 | 11.4 | 11.1 | 13.4 | 8.6 | 14.2 | 8.9 | 1.1 | | 34.7 | 2.5 | 9.5 | 20.3 | 19.3 | 12.8 | 7.1 | 8.3 | 10.6 | 9.6 | 10.9 | 11 | 9.2 | 8.1 | 14.7 | 9.9 | 1 | | 25.7 | 2.4 | 9.4 | 20.1 | 20.5 | 12.8 | 7.8 | 8.5 | 11 | 9.4 | 10.6 | 11 | 11 | 8 | 14.5 | 9.7 | 0.3 | | 50.8 | 2.4 | 9.3 | 20.1 | 20.3 | 12.7 | -2 | 8.6 | 11 | 9.3 | 12.6 | 11.3 | 12.1 | 7.5 | 14 | 9 | 0.1 | | 37.9 | 2.4 | 9.4 | 20.4 | 20.6 | 12.6 | 0.4 | 10.9 | 10.2 | 9.2 | 13.3 | 11.3 | 6.2 | 6.6 | 15.8 | 11.3 | 0.8 | | 30.7 | 2.4 | 9.4 | 20.6 | 21.1 | 12.5 | 15.7 | 14 | 8.5 | 8.9 | 10.9 | 10.7 | 6.1 | 6.7 | 16.1 | 11.9 | 0.6 |
求后面数据关于ZKR的线性关系
x<-read.csv("R_mon_final1.csv",header=T,sep=",")
x.pr<-princomp(~CPIR+GYLJ+GDTZ+FDC+SHXFLJ+JCK+KCKLJ+HYL+HYLLJ+GLHY+GLHYLJ+PMI_R+CZ+CZLJ+M2+M1,data=x,cor=T)
summary(x.pr,loadings=TRUE)#找出所有变量之间的关系，获得主成分概况
#对主成分做回归分析
pre=predict(x.pr)
x$Z1=pre[,1]
x$Z2=pre[,2]
x$Z3=pre[,3]
x$Z4=pre[,4]
x$Z5=pre[,5]
x$Z6=pre[,6]
x$Z7=pre[,7]
lm.sol=lm(ZKR~Z1+Z2+Z3+Z4+Z5+Z6+Z7,data=x)
summary(lm.sol)
#Z4，Z5,Z7与ZKR的相关性较低，去除
lm.sol=lm(ZKR~Z1+Z2+Z3+Z6,data=x)
summary(lm.sol)
#找出最终的系数
beta=coef(lm.sol)#主成分的系数
A=loadings(x.pr)#主成分与各变量之间的关系
X.bar=x.pr$center#标准化时的中心值
X.sd=x.pr$scale#标准化时的方差
coef=(beta[2]*A[,1]+beta[3]*A[,2]+beta[4]*A[,3]+beta[5]*A[,6])/X.sd
beta0=beta[1]-sum(X.bar*coef)
c(beta0,coef)
#进行验算
x1<-read.csv("R_mon_final1.csv",header=T,sep=",")
Mtr_x<-as.matrix(x1[,-1])
lm_ZKR<-Mtr_x%*%coef + beta0
[1,] -45.04046
[2,] -40.99504
[3,] -81.49026
[4,] -89.34061
[5,] -76.77624
[6,] -91.25612
[7,] -82.68502
[8,] -127.78570
[9,] -153.36960
[10,] -100.70537
[11,] -119.28412
[12,] -103.11603
[13,] -84.61337
[14,] -84.74305
[15,] -92.07408
[16,] -92.04162
[17,] -98.76406
[18,] -77.71950
[19,] -62.91335
[20,] -61.15001
[21,] -61.84735
[22,] -63.64842
[23,] -72.89045
[24,] -40.98089
[25,] -50.51999
[26,] -109.60241
[27,] -34.53901
[28,] -78.30246
[29,] -80.44856
[30,] -66.16390
[31,] -44.93860
[32,] -58.60507
[33,] -61.70416
[34,] -22.53547
[35,] -33.38052
用主成分聚合并画图，拟合非常好，但是系数还原后就不行了，求助。
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发表于 2016-4-19 12:35:10