Volba správného počtu hlavních komponent

Machine Learning for Marketing Analytics in R

Verena Pflieger

Data Scientist at INWT Statistics

Počet komponent: vysvětlený rozptyl

# Proportion of variance explained:
summary(pcaCust)

Importance of components:
                          PC1    PC2    PC3     PC4     PC5     PC6     PC7
Standard deviation     2.0951 1.8379 1.2960 1.20415 1.12301 1.07453 0.80486
Proportion of Variance 0.2743 0.2111 0.1050 0.09062 0.07882 0.07216 0.04049
Cumulative Proportion  0.2743 0.4855 0.5904 0.68106 0.75989 0.83205 0.87254
                           PC8     PC9    PC10    PC11    PC12    PC13
Standard deviation     0.78236 0.72452 0.61302 0.48428 0.36803 0.25901
Proportion of Variance 0.03826 0.03281 0.02349 0.01466 0.00847 0.00419
Cumulative Proportion  0.91079 0.94360 0.96709 0.98175 0.99021 0.99440
                          PC14    PC15    PC16
Standard deviation     0.20699 0.17126 0.13170
Proportion of Variance 0.00268 0.00183 0.00108
Cumulative Proportion  0.99708 0.99892 1.00000

Počet komponent: Kaiserovo-Guttmanovo kritérium

Kaiserovo-Guttmanovo kritérium: vlastní číslo > 1

pcaCust$sdev ^ 2

 [1] 4.38961593 3.37778445 1.67965616 1.44997580 1.26115351 1.15461579
 [7] 0.64780486 0.61209376 0.52492468 0.37579685 0.23452736 0.13544710
[13] 0.06708362 0.04284504 0.02933027 0.01734481

Počet komponent: screeplot

Screeplot neboli: „Najdi loket"

screeplot(pcaCust, type = "lines")
box()
abline(h = 1, lty = 2)

Doporučený počet komponent podle kritéria

Vysvětlený rozptyl	Kaiser-Guttman	Screeplot
5	6	6

Biplot

biplot(pcaCust, choices = 1:2, cex = 0.7)

Hands on!

Machine Learning for Marketing Analytics in R