# Which variables to keep in my analysis based on loadings from PCA? [duplicate]

Could someone please explain me how I should decide which variables to keep in my analysis based on loadings from PCA. The output is:

    Comp.1     Comp.2    Comp.3  Comp.4     Comp.5
a   0.0281003 0.37882295 0.2935517 0.02025596 0.11199220
b   0.2019940 0.21168386 0.2398182 0.37883484 0.03540004
c   0.2545871 0.20163264 0.1459563 0.07187896 0.39797528
d   0.2774044 0.05867002 0.1859529 0.06134311 0.41428056
e   0.2379143 0.14919053 0.1347208 0.46768713 0.04035192

Importance of components:
Comp.1    Comp.2    Comp.3     Comp.4     Comp.5
Standard deviation     1.5809667 1.0987927 0.8806842 0.63815856 0.33218647
Proportion of Variance 0.4998911 0.2414691 0.1551209 0.08144927 0.02206957
Cumulative Proportion  0.4998911 0.7413602 0.8964812 0.97793043 1.00000000


Does this mean that variable a is not important and I can drop it? Is there any method for making this decision?

• Too scarce information (and effort). Keep for what? "Interpretation" - in what sense? Important - how? Please pardon me for an advice for you to read some textbook on PCA first. Mar 3, 2015 at 12:39
• It's hard to give advices without knowing why you want to drop any variables at all. But note that variable a contributes strongly to PC2, which has variance comparable to PC1. Mar 3, 2015 at 15:17

Based on the standard deviation it looks like PC1 and PC2 could be considered significant, with a contributing strongly to PC2 (relative to the other variables). Let's say you chose your cutoff as $.25$ - PC1 would be related to variables c and d, while PC2 would be related to variable a alone. In other words, you should not throw a away - it looks like it has a potentially strong association with PC2 and has the highest loading value of any variable on the first two PCs.
Without any other information, yes, if you need to drop a variable, that would be $a$.