# Why do PCA scores differ in sign?

Consider this:

set.seed(999)
prcomp(data.frame(1:10,rnorm(10)))$x PC1 PC2 [1,] -4.508620 -0.2567655 [2,] -3.373772 -1.1369417 [3,] -2.679669 1.0903445 [4,] -1.615837 0.7108631 [5,] -0.548879 0.3093389 [6,] 0.481756 0.1639112 [7,] 1.656178 -0.9952875 [8,] 2.560345 -0.2490548 [9,] 3.508442 0.1874520 [10,] 4.520055 0.1761397 set.seed(999) princomp(data.frame(1:10,rnorm(10)))$scores
Comp.1     Comp.2
[1,]  4.508620  0.2567655
[2,]  3.373772  1.1369417
[3,]  2.679669 -1.0903445
[4,]  1.615837 -0.7108631
[5,]  0.548879 -0.3093389
[6,] -0.481756 -0.1639112
[7,] -1.656178  0.9952875
[8,] -2.560345  0.2490548
[9,] -3.508442 -0.1874520
[10,] -4.520055 -0.1761397


set.seed(999)
eigen(cov(data.frame(1:10,rnorm(10))))


Why do the signs (+/-) differ for the two analyses? If I was then fitting PCA1 and PCA2 to another variable y, i.e. lm(y ~ PCA1 + PCA2), this would completely change my understanding of the effect of the two variables on y depending on which method I used!

How can I then say that PCA1 has a positive effect on y and PCA2 has a negative effect on y?

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PCA is a simple mathematical transformation. If you change the signs of the component(s), you do not change the variance that is contained in the first component. Moreover, when you change the signs, the weights (prcomp( ... )$rotation) also change the sign, so the interpretation stays exactly the same: set.seed( 999 ) a <- data.frame(1:10,rnorm(10)) pca1 <- prcomp( a ) pca2 <- princomp( a ) pca1$rotation


shows

                 PC1       PC2
X1.10      0.9900908 0.1404287
rnorm.10. -0.1404287 0.9900908


and pca2$loadings show Loadings: Comp.1 Comp.2 X1.10 -0.99 -0.14 rnorm.10. 0.14 -0.99 Comp.1 Comp.2 SS loadings 1.0 1.0 Proportion Var 0.5 0.5 Cumulative Var 0.5 1.0  So, why does the interpretation stays the same? You do the PCA regression of y on component 1. In the first version (prcomp), say the coefficient is positive: the larger the component 1, the larger the y. What does it mean when it comes to the original variables? Since the weight of the variable 1 (1:10 in a) is positive, that shows that the larger the variable 1, the larger the y. Now use the second version (princomp). Since the component has the sign changed, the larger the y, the smaller the component 1 -- the coefficient of y< over PC1 is now negative. But so is the loading of the variable 1; that means, the larger variable 1, the smaller the component 1, the larger y -- the interpretation is the same. Possibly, the easiest way to see that is to use a biplot. library( pca3d ) pca2d( pca1, biplot= TRUE, shape= 19, col= "black" )  shows The same biplot for the second variant shows pca2d( pca2$scores, biplot= pca2$loadings[,], shape= 19, col= "black" )  As you see, the images are rotated by 180°. However, the relation between the weights / loadings (the red arrows) and the data points (the black dots) is exactly the same; thus, the interpretation of the components is unchanged. - ok much clearer thank you for your edit! – user1320502 Mar 5 at 12:43 I even added pictures now :-) – January Mar 5 at 12:50 This was well answered above. Just to provide some further mathematical relevance, the directions that the principal components act correspond to the eigenvectors of the system. If you are getting a positive or negative PC it just means that you are projecting on an eigenvector that is pointing in one direction or$180^\circ\$ away in the other direction. Regardless, the interpretation remains the same! It should also be added that the lengths of your principal components are simply the eigenvalues.

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