Page 222 - MATLAB Recipes for Earth Sciences

218 9 Multivariate Statistics

Correlation Matrix
+ 1.0
gal
sph
+ 0.5
flu

cla

qtz 0

ksp

pla
− 0.5
pyr

amp
− 1.0
amp pyr pla ksp qtz cla flu sph gal

Fig. 9.2 Correlation matrix containing Pearson·s correlation coefficients for each pair of
variables, such as minerals in a sediment sample. Light colors represent strong positive
linear correlations, whereas dark colors document negative correlations. Orange suggests
no correlation.
PCA, such as mean centering (zero means) or autoscaling (mean zero and
standard deviation equals one). However, we use the original data for com-
puting the PCA. The output of the function princomp includes the principal
components pcs, the component scores of the data newdata and the com-
ponent variances.

[pcs,newdata,variances] = princomp(data);

The fi rst five principal components PC to PC can be shown ty typing
1 5
pcs(:,1:5)
ans =
-0.3303 0.2963 -0.4100 -0.5971 0.1380
-0.3557 0.0377 0.6225 0.2131 0.5251
-0.5311 0.1865 -0.2591 0.4665 -0.3010
0.1410 0.1033 -0.0175 0.0689 -0.3367
0.6334 0.4666 -0.0351 0.1629 0.1794
0.1608 0.2097 0.2386 -0.0513 -0.2503
0.1673 -0.4879 -0.4978 0.2287 0.4756
0.0375 -0.2722 0.2392 -0.5403 -0.0068
0.0771 -0.5399 0.1173 0.0480 -0.4246