获得pca中的累积比例

Question

我希望在R中的pca之后summary(pca)将最终解释的方差的累积比例返回到最后一行,但是如何提取该行呢？

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

我尝试了s <- summary(prcomp(USArrests, scale = TRUE)) s [3]等,但它没有返回最后一行.

Answer 1

你可以试试

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

Answer 2

您也可以直接从特征值(即标准开发:)中提取此信息pr$sdev:

pr <- prcomp(USArrests, scale = TRUE)
cumsum(pr$sdev^2 / sum(pr$sdev^2))

Answer 3

扩展问题评论中 user20650 的答案，因为我相信它最直接地回答了问题（即通过对象本身，而不是重新计算）。长话短说：博士：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