Dear Rusers,

  I have met a difficult problem on explaining the differences of principal
component analysis(PCA) between R,S-PLUS and SAS/STATA/SPSS, which wasn't
met before.

  Althought they have got the same eigenvalues, their coeffiecients were
different.

  First, I list my results from R,S-PLUS and SAS/STATA/SPSS, and then show
the original dataset, hoping sb. to try and explain it.

  SAS,STATA,and SPSS have the same results, so i put them together. From
their results, we see that the absolute values of coeffiecient are same, but
PC1,PC2,PC4,PC5 and PC6 in R have the opposite sign on the coeffiecnts
contrast with SAS, and PC4,PC5 in S-PLUS have the opposite sign on the
coeffiecnts contrast with SAS. Curiously, I got the same results amont all
these software using my another dataset.

*R's results of PCA:*

      *PC1*         *PC2*          PC3        *PC4*        *PC5 *        *
PC6*

X1 -0.5152569  0.20264489 -0.2338786  0.2350876 -0.2033335 -0.736298528

X2 -0.5197856  0.08989351 -0.2068260  0.3737667 -0.3187746  0.661548469

X3 -0.5148033  0.15820613 -0.0590627 -0.3210113  0.7693052  0.107616466

X4 -0.3535798  0.08105168  0.7317188 -0.4350752 -0.3790772  0.003088541

X5 -0.1868691 -0.67517084 -0.4397442 -0.5119015 -0.2314833 -0.014886524

X6 -0.1984241 -0.68073489  0.4126112  0.5006500  0.2606219 -0.091682326



pca<-read.csv('D:\pca.csv',sep=',',header=T)

attach(pca)

pcacomp <- prcomp(pca[,-1], retx=TRUE, center=TRUE,scale.= TRUE,tol=0.0001)



*S-Plus's results of PCA:*

     pc1     pc2      pc3     *pc4    pc5*     pc6

X1  0.5153 -0.2026 -0.2339  0.2351 -0.2033  0.7363

X2  0.5198 -0.0899 -0.2068  0.3738 -0.3188 -0.6615

X3  0.5148 -0.1582 -0.0591 -0.3210  0.7693 -0.1076

X4  0.3536 -0.0811  0.7317 -0.4351 -0.3791 -0.0031

X5  0.1869  0.6752 -0.4397 -0.5119 -0.2315  0.0149

X6  0.1984  0.6807  0.4126  0.5007  0.2606  0.0917



*SAS/STATA/SPSS's results of PCA:*

     PC1       PC2     PC3      PC4      PC5       PC6

X1 0.515257 -.202645 -.233879 -.235088 0.203334 0.736299

X2 0.519786 -.089894 -.206826 -.373767 0.318775 -.661548

X3 0.514803 -.158206 -.059063 0.321011 -.769305 -.107616

X4 0.353580 -.081052 0.731719 0.435075 0.379077 -.003089

X5 0.186869 0.675171 -.439744 0.511902 0.231483 0.014887

X6 0.198424 0.680735 0.412611 -.500650 -.260622 0.091682



My dataset used in the above results is :

   X1

X2

X3

X4

X5

X6

173.28

93.62

60.1

86.72

38.97

27.51

172.09

92.83

60.38

87.39

38.62

27.82

171.46

92.73

59.74

85.59

38.83

27.46

170.08

92.25

58.04

85.92

38.33

27.29

170.61

92.36

59.67

87.46

38.38

27.14

171.69

92.85

59.44

87.45

38.19

27.1

171.46

92.93

58.7

87.06

38.58

27.36

171.6

93.28

59.75

88.03

38.68

27.22

171.6

92.26

60.5

87.63

38.79

26.63

171.16

92.62

58.72

87.11

38.19

27.18

170.04

92.17

56.95

88.08

38.24

27.65

170.27

91.94

56

84.52

37.16

26.81

170.61

92.5

57.34

85.61

38.52

27.36

171.39

92.44

58.92

85.37

38.83

26.47

171.83

92.79

56.85

85.35

38.58

27.03

171.36

92.53

58.39

87.09

38.23

27.04

171.24

92.61

57.69

83.98

39.04

27.07

170.49

92.03

57.56

87.18

38.54

27.57

169.43

91.67

55.22

83.87

38.41

26.6

168.57

91.4

55.96

83.02

38.74

26.97

170.43

92.38

57.87

84.87

38.78

27.37

169.88

91.89

56.87

86.34

38.37

27.19

167.94

90.91

55.97

86.77

38.17

27.16

168.82

91.3

56.07

85.87

37.61

26.67

168.02

91.26

55.28

85.63

39.66

28.07

167.87

90.96

55.79

84.92

38.2

26.53

168.15

91.5

54.56

84.81

38.44

27.38

168.99

91.52

55.11

86.23

38.3

27.11

 Any help or suggestions are greatly appreciated.


-- 
With Kind Regards,

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Zhi Jie,Zhang ,PHD
Tel:86-21-54237149   epistat@gmail.com
Dept. of Epidemiology,school of public health,Fudan University
Address:No. 138 Yi Xue Yuan Road,Shanghai,China
Postcode:200032
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