1. Canonical Correlation analysis R
Test<-read.csv ("D:\\data\\hongputao_l.csv", header=T) Test2<-scale (test[,1:Ten]) CA<-cancor (test2[,1:8],test2[,9:Ten]) #由ca分析结果可知典型变量应选1, 22 pair U<- as. Matrix (test2[,1:8]) %*%CA$XCOEFV<- as. Matrix (test2[,9:Ten]) %*%Ca$ycoefplot (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$cor,n= -, p=3, q=3)>ca$cor[1]0.9213551 0.5886030$xcoef [,1] [,2] [,3] [,4] Main Ingredient 10.163498177 0.079948250-0.039715201 0.0158801038X20.040681260 0.014842622 0.184701392-0.0097196244X30.075116846-0.172302831 0.010676318 0.0150943148X4-0.018458341 0.008431296 0.012791371 0.1945832677X5-0.005089435-0.016013333-0.014198980 0.0007542678X60.026995286-0.026846990-0.032739004 0.0031734533X70.057372365 0.005033856-0.009561037 0.0027595133X80.006740071-0.032979314-0.033573241 0.0024787198 [,5] [,6] [,7] [,8] Main Ingredient 10.0076888774-0.020604651-0.0549986881 0.0004009247X20.0166091983 0.029483915-0.0026492560-0.0350001342X3-0.0123186358-0.035120994-0.0188409850 0.0323835548X40.0004484457 0.002930929 0.0032809836-0.0019793391X50.1948450920-0.002273650 0.0001750191 0.0026868876X6-0.0017595125 0.189389465-0.0053759902 0.0054545766X70.0009826550-0.003860236 0.1871611541 0.0006461388X8-0.0024376501-0.006095797-0.0023874170-0.1901219807$ycoef [,1] [,2] Main ingredient 1.1 0.19129779-0.04320525score 20.04320498 0.19129785
Mathematical modeling Algorithm (II.): Correlation analysis