Information Technology Reference
In-Depth Information
0.30
0.35
0.5
0.6
0.25
0.4
0.20
0.3
0.15
0.2
0.10
0.1
0.05
0.00
0.0
X1 (0.42)
X1 (0.06)
X2 (0.9)
X2 (0.05)
X3 (0.08)
X4 (0.1)
X3 (0.02)
X4 (0)
X5 (0)
X5 (0.02)
X6 (0.02)
X7 (0.04)
X6 (0)
X7 (0)
X8 (0.61)
X8 (0.28)
1.2
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
X1 (0.02)
X2 (0)
X1 (0)
X2 (0)
X3 (0)
X3 (0.06)
X4 (0.02)
X4 (0.06)
X5 (0.04)
X5 (0.01)
X6 (0.01)
X6 (0.03)
X7 (0.14)
X7 (0)
X8 (0)
X8 (0)
Figure 3.39
One-dimensional PCA biplots of copper froth data: (top left) second principal component scores; (top right) third
principal component scores; (bottom left) fourth principal component scores; (bottom right) fifth principal component scores.
These four biplots are constructed by assigning in the call to
PCAbipl
argument
e.vects
successively the single integer values
2, 3, 4 and 5.
