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Jun 27th, 2019
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  1. names(mydata)
  2. [1] "A" "B" "C" "D" "E" "F" "G"
  3.  
  4. > x<-cbind(A, B, C, D, E, F, G)
  5. > e_value<-eigen(cor(x))
  6. > e_value
  7. eigen() decomposition
  8. $values
  9. [1] 2.3502254 1.4170606 1.2658360 0.8148231 0.5608698 0.3438629 0.2473222
  10.  
  11. $vectors
  12. [,1] [,2] [,3] [,4] [,5]
  13. [,6] [,7]
  14. [1,] 0.2388621 0.46839043 0.37003850 0.47205027 -0.58802244
  15. -0.133939151 -0.009233395
  16. [2,] 0.1671739 -0.71097984 -0.14062597 0.25083439 -0.26726985
  17. -0.502411130 -0.244983436
  18. [3,] 0.2132841 -0.19677142 0.64662974 0.34508779 0.61416969
  19. -0.003950736 0.036814153
  20. [4,] 0.1697817 -0.24468987 0.55631886 -0.69016805 -0.34039757
  21. 0.039899816 0.089531675
  22. [5,] 0.4857016 0.36681570 -0.09905329 -0.31456085 0.26225761
  23. -0.344919726 -0.577088755
  24. [6,] -0.5359245 0.20164924 0.17958243 -0.13144417 0.11755661
  25. -0.748885304 0.218966481
  26. [7,] 0.5635252 0.03619081 -0.27131854 -0.05105919 0.08439733
  27. -0.219629096 0.741315659
  28.  
  29.  
  30. > PCA<-principal(x,nfactors = 3, rotate = "varimax")
  31. > print(PCA)
  32. Principal Components Analysis
  33. Call: principal(r = x, nfactors = 3, rotate = "varimax")
  34. Standardized loadings (pattern matrix) based upon correlation matrix
  35. RC1 RC2 RC3 h2 u2 com
  36. A 0.24 0.69 0.29 0.62 0.38 1.6
  37. B 0.25 -0.83 0.24 0.81 0.19 1.3
  38. C 0.06 0.05 0.83 0.69 0.31 1.0
  39. D 0.03 -0.04 0.74 0.54 0.46 1.0
  40. E 0.76 0.42 -0.01 0.76 0.24 1.5
  41. F -0.83 0.24 -0.17 0.77 0.23 1.3
  42. G 0.92 -0.01 0.00 0.84 0.16 1.0
  43.  
  44. RC1 RC2 RC3
  45. SS loadings 2.23 1.40 1.40
  46. Proportion Var 0.32 0.20 0.20
  47. Cumulative Var 0.32 0.52 0.72
  48. Proportion Explained 0.44 0.28 0.28
  49. Cumulative Proportion 0.44 0.72 1.00
  50.  
  51. Mean item complexity = 1.3
  52. Test of the hypothesis that 3 components are sufficient.
  53.  
  54. The root mean square of the residuals (RMSR) is 0.11
  55. with the empirical chi square 63.33 with prob < 1.1e-13
  56.  
  57. Fit based upon off diagonal values = 0.84
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