What you are looking for is in the "element" importance
of summary(res.pca)
:
Example taken from Principal Components Analysis - how to get the contribution (%) of each parameter to a Prin.Comp.?:
a <- rnorm(10, 50, 20)
b <- seq(10, 100, 10)
c <- seq(88, 10, -8)
d <- rep(seq(3, 16, 3), 2)
e <- rnorm(10, 61, 27)
my_table <- data.frame(a, b, c, d, e)
res.pca <- prcomp(my_table, scale = TRUE)
summary(res.pca)$importance
# PC1 PC2 PC3 PC4 PC5
#Standard deviation 1.7882 0.9038 0.8417 0.52622 9.037e-17
#Proportion of Variance 0.6395 0.1634 0.1417 0.05538 0.000e+00
#Cumulative Proportion 0.6395 0.8029 0.9446 1.00000 1.000e+00
class(summary(res.pca)$importance)
#[1] "matrix"
N.B.:
When you want to "study" an object, it can be convenient to use str
on it. Here, you can do str(summary(pca)
to see where the information are and hence where you can get what you want:
str(summary(res.pca))
List of 6
$ sdev : num [1:5] 1.79 9.04e-01 8.42e-01 5.26e-01 9.04e-17
$ rotation : num [1:5, 1:5] 0.278 0.512 -0.512 0.414 -0.476 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:5] "a" "b" "c" "d" ...
.. ..$ : chr [1:5] "PC1" "PC2" "PC3" "PC4" ...
$ center : Named num [1:5] 34.9 55 52 9 77.8
..- attr(*, "names")= chr [1:5] "a" "b" "c" "d" ...
$ scale : Named num [1:5] 22.4 30.28 24.22 4.47 26.11
..- attr(*, "names")= chr [1:5] "a" "b" "c" "d" ...
$ x : num [1:10, 1:5] -2.962 -1.403 -1.653 -0.537 1.186 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr [1:5] "PC1" "PC2" "PC3" "PC4" ...
$ importance: num [1:3, 1:5] 1.788 0.64 0.64 0.904 0.163 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:3] "Standard deviation" "Proportion of Variance" "Cumulative Proportion"
.. ..$ : chr [1:5] "PC1" "PC2" "PC3" "PC4" ...
- attr(*, "class")= chr "summary.prcomp"