1.典型相关分析 R
test<-read.csv("D:\\data\\hongputao_l.csv",header=T) test2<-scale(test[,1:10]) ca<-cancor(test2[,1:8],test2[,9:10]) #由ca分析结果可知典型变量应选1、2两对 U<-as.matrix(test2[, 1:8]) %*% ca$xcoef V<-as.matrix(test2[, 9:10]) %*% ca$ycoef plot(U[,1], V[,1], xlab="U1", ylab="V1") plot(U[,2], V[,2], xlab="U2", ylab="V2") #相关系数检验R程序 source("D:/data/R/corcoef.test.R") corcoef.test(r=ca$cor,n=20,p=3,q=3) > ca $cor [1] 0.9213551 0.5886030 $xcoef [,1] [,2] [,3] [,4] 主成分1 0.163498177 0.079948250 -0.039715201 0.0158801038 X2 0.040681260 0.014842622 0.184701392 -0.0097196244 X3 0.075116846 -0.172302831 0.010676318 0.0150943148 X4 -0.018458341 0.008431296 0.012791371 0.1945832677 X5 -0.005089435 -0.016013333 -0.014198980 0.0007542678 X6 0.026995286 -0.026846990 -0.032739004 0.0031734533 X7 0.057372365 0.005033856 -0.009561037 0.0027595133 X8 0.006740071 -0.032979314 -0.033573241 0.0024787198 [,5] [,6] [,7] [,8] 主成分1 0.0076888774 -0.020604651 -0.0549986881 0.0004009247 X2 0.0166091983 0.029483915 -0.0026492560 -0.0350001342 X3 -0.0123186358 -0.035120994 -0.0188409850 0.0323835548 X4 0.0004484457 0.002930929 0.0032809836 -0.0019793391 X5 0.1948450920 -0.002273650 0.0001750191 0.0026868876 X6 -0.0017595125 0.189389465 -0.0053759902 0.0054545766 X7 0.0009826550 -0.003860236 0.1871611541 0.0006461388 X8 -0.0024376501 -0.006095797 -0.0023874170 -0.1901219807 $ycoef [,1] [,2] 主成分1.1 0.19129779 -0.04320525 得分2 0.04320498 0.19129785
