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- names(mydata)
- [1] "A" "B" "C" "D" "E" "F" "G"
- > x<-cbind(A, B, C, D, E, F, G)
- > e_value<-eigen(cor(x))
- > e_value
- eigen() decomposition
- $values
- [1] 2.3502254 1.4170606 1.2658360 0.8148231 0.5608698 0.3438629 0.2473222
- $vectors
- [,1] [,2] [,3] [,4] [,5]
- [,6] [,7]
- [1,] 0.2388621 0.46839043 0.37003850 0.47205027 -0.58802244
- -0.133939151 -0.009233395
- [2,] 0.1671739 -0.71097984 -0.14062597 0.25083439 -0.26726985
- -0.502411130 -0.244983436
- [3,] 0.2132841 -0.19677142 0.64662974 0.34508779 0.61416969
- -0.003950736 0.036814153
- [4,] 0.1697817 -0.24468987 0.55631886 -0.69016805 -0.34039757
- 0.039899816 0.089531675
- [5,] 0.4857016 0.36681570 -0.09905329 -0.31456085 0.26225761
- -0.344919726 -0.577088755
- [6,] -0.5359245 0.20164924 0.17958243 -0.13144417 0.11755661
- -0.748885304 0.218966481
- [7,] 0.5635252 0.03619081 -0.27131854 -0.05105919 0.08439733
- -0.219629096 0.741315659
- > PCA<-principal(x,nfactors = 3, rotate = "varimax")
- > print(PCA)
- Principal Components Analysis
- Call: principal(r = x, nfactors = 3, rotate = "varimax")
- Standardized loadings (pattern matrix) based upon correlation matrix
- RC1 RC2 RC3 h2 u2 com
- A 0.24 0.69 0.29 0.62 0.38 1.6
- B 0.25 -0.83 0.24 0.81 0.19 1.3
- C 0.06 0.05 0.83 0.69 0.31 1.0
- D 0.03 -0.04 0.74 0.54 0.46 1.0
- E 0.76 0.42 -0.01 0.76 0.24 1.5
- F -0.83 0.24 -0.17 0.77 0.23 1.3
- G 0.92 -0.01 0.00 0.84 0.16 1.0
- RC1 RC2 RC3
- SS loadings 2.23 1.40 1.40
- Proportion Var 0.32 0.20 0.20
- Cumulative Var 0.32 0.52 0.72
- Proportion Explained 0.44 0.28 0.28
- Cumulative Proportion 0.44 0.72 1.00
- Mean item complexity = 1.3
- Test of the hypothesis that 3 components are sufficient.
- The root mean square of the residuals (RMSR) is 0.11
- with the empirical chi square 63.33 with prob < 1.1e-13
- Fit based upon off diagonal values = 0.84
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