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 [EMAIL PROTECTED]
Dept. of Epidemiology,school of public health,Fudan University
Address:No. 138 Yi Xue Yuan Road,Shanghai,China
Postcode:200032
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