# [ACCEPTED]-Getting cumulative proportion in pca-r

Score: 14

You can try

``````pr <- prcomp(USArrests, scale = TRUE)
vars <- apply(pr\$x, 2, var)
props <- vars / sum(vars)
cumsum(props)
``````

0

Score: 13

You can also extract this information directly 1 from the eigenvalues (i.e. standard dev: `pr\$sdev`):

``````pr <- prcomp(USArrests, scale = TRUE)
cumsum(pr\$sdev^2 / sum(pr\$sdev^2))
``````
Score: 6

Expanding on user20650's answer in the question's 3 comments, as I believe it answers the question 2 most directly (i.e. via the object itself, rather 1 than recalculating). TL;DR: `s\$importance[3, ]`.

``````(s <- summary(prcomp(USArrests, scale = TRUE)))
# Importance of components:
#                           PC1    PC2     PC3     PC4
# Standard deviation     1.5749 0.9949 0.59713 0.41645
# Proportion of Variance 0.6201 0.2474 0.08914 0.04336
# Cumulative Proportion  0.6201 0.8675 0.95664 1.00000

str(s)
# List of 6
#  \$ sdev      : num [1:4] 1.575 0.995 0.597 0.416
#  \$ rotation  : num [1:4, 1:4] -0.536 -0.583 -0.278 -0.543 0.418 ...
#   ..- attr(*, "dimnames")=List of 2
#   .. ..\$ : chr [1:4] "Murder" "Assault" "UrbanPop" "Rape"
#   .. ..\$ : chr [1:4] "PC1" "PC2" "PC3" "PC4"
#  \$ center    : Named num [1:4] 7.79 170.76 65.54 21.23
#   ..- attr(*, "names")= chr [1:4] "Murder" "Assault" "UrbanPop" "Rape"
#  \$ scale     : Named num [1:4] 4.36 83.34 14.47 9.37
#   ..- attr(*, "names")= chr [1:4] "Murder" "Assault" "UrbanPop" "Rape"
#  \$ x         : num [1:50, 1:4] -0.976 -1.931 -1.745 0.14 -2.499 ...
#   ..- attr(*, "dimnames")=List of 2
#   .. ..\$ : chr [1:50] "Alabama" "Alaska" "Arizona" "Arkansas" ...
#   .. ..\$ : chr [1:4] "PC1" "PC2" "PC3" "PC4"
#  \$ importance: num [1:3, 1:4] 1.575 0.62 0.62 0.995 0.247 ...
#   ..- attr(*, "dimnames")=List of 2
#   .. ..\$ : chr [1:3] "Standard deviation" "Proportion of Variance" "Cumulative Proportion"
#   .. ..\$ : chr [1:4] "PC1" "PC2" "PC3" "PC4"
#  - attr(*, "class")= chr "summary.prcomp"

# We see importance is the relevant feature
s\$importance
#                             PC1       PC2       PC3       PC4
# Standard deviation     1.574878 0.9948694 0.5971291 0.4164494
# Proportion of Variance 0.620060 0.2474400 0.0891400 0.0433600
# Cumulative Proportion  0.620060 0.8675000 0.9566400 1.0000000

# Cool, same as displayed the table. Grab the third row and voila.
s\$importance[3, ]  # Numeric vector
#     PC1     PC2     PC3     PC4
# 0.62006 0.86750 0.95664 1.00000
``````
Score: 0

```R » s <- as.data.frame( summary(prcomp(USArrests, scale=TRUE))\$importance ) R » s[3, 1:2] PC1 PC2 Cumulative Proportion 0.6201 0.8675 R » message("PC1: ", s[3,1]) PC1: 0.62006 ```