Re: [R] R and S-Plus got the different results of principal component analysis from SAS, why?

From: Gavin Simpson <gavin.simpson_at_ucl.ac.uk>
Date: Tue 30 Jan 2007 - 15:08:28 GMT

On Tue, 2007-01-30 at 22:29 +0800, zhijie zhang wrote:
> 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.

Only up to rounding of printed results and their signs, which are arbitrary. The latter is covered in both the help pages for the two main PCA functions in R. Please read the Notes section of ?prcomp and/or ?princomp (you don't say which you used in R).

G

>
> 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,
>
> oooO:::::::::
> (..):::::::::
> :\.(:::Oooo::
> ::\_)::(..)::
> :::::::)./:::
> ::::::(_/::::
> :::::::::::::
> [***********************************************************************]
> 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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> (..):::::::::
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> ::\_)::(..)::
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> [[alternative HTML version deleted]]
>
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 Gavin Simpson                 [t] +44 (0)20 7679 0522
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Received on Wed Jan 31 02:33:44 2007

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