Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for prcomp tidy(x, matrix = "u", ...)
x | A |
---|---|
matrix | Character specifying which component of the PCA should be tidied.
|
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble with columns depending on the component of PCA being tidied.
If matrix
is "u"
, "samples"
, "scores"
, or "x"
each row in the
tidied output corresponds to the original data in PCA space. The columns
are:
row
ID of the original observation (i.e. rowname from original data).
PC
Integer indicating a principal component.
value
The score of the observation for that particular principal component. That is, the location of the observation in PCA space.
row
The variable labels (colnames) of the data set on which PCA was performed
PC
An integer vector indicating the principal component
value
The value of the eigenvector (axis score) on the indicated principal component
PC
An integer vector indicating the principal component
std.dev
Standard deviation explained by this PC
percent
Fraction of variation explained by this component (a numeric value between 0 and 1).
cumulative
Cumulative fraction of variation explained by principle components up to this component (a numeric value between 0 and 1).
See https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca for information on how to interpret the various tidied matrices. Note that SVD is only equivalent to PCA on centered data.
Other svd tidiers:
augment.prcomp()
,
tidy_irlba()
,
tidy_svd()
pc <- prcomp(USArrests, scale = TRUE) # information about rotation tidy(pc) #> # A tibble: 200 × 3 #> row PC value #> <chr> <dbl> <dbl> #> 1 Alabama 1 -0.976 #> 2 Alabama 2 1.12 #> 3 Alabama 3 -0.440 #> 4 Alabama 4 0.155 #> 5 Alaska 1 -1.93 #> 6 Alaska 2 1.06 #> 7 Alaska 3 2.02 #> 8 Alaska 4 -0.434 #> 9 Arizona 1 -1.75 #> 10 Arizona 2 -0.738 #> # … with 190 more rows # information about samples (states) tidy(pc, "samples") #> # A tibble: 200 × 3 #> row PC value #> <chr> <dbl> <dbl> #> 1 Alabama 1 -0.976 #> 2 Alabama 2 1.12 #> 3 Alabama 3 -0.440 #> 4 Alabama 4 0.155 #> 5 Alaska 1 -1.93 #> 6 Alaska 2 1.06 #> 7 Alaska 3 2.02 #> 8 Alaska 4 -0.434 #> 9 Arizona 1 -1.75 #> 10 Arizona 2 -0.738 #> # … with 190 more rows # information about PCs tidy(pc, "pcs") #> # A tibble: 4 × 4 #> PC std.dev percent cumulative #> <dbl> <dbl> <dbl> <dbl> #> 1 1 1.57 0.620 0.620 #> 2 2 0.995 0.247 0.868 #> 3 3 0.597 0.0891 0.957 #> 4 4 0.416 0.0434 1 # state map library(dplyr) library(ggplot2) pc %>% tidy(matrix = "samples") %>% mutate(region = tolower(row)) %>% inner_join(map_data("state"), by = "region") %>% ggplot(aes(long, lat, group = group, fill = value)) + geom_polygon() + facet_wrap(~PC) + theme_void() + ggtitle("Principal components of arrest data") au <- augment(pc, data = USArrests) au #> # A tibble: 50 × 9 #> .rownames Murder Assault UrbanPop Rape .fittedPC1 .fittedPC2 .fittedPC3 #> <chr> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl> #> 1 Alabama 13.2 236 58 21.2 -0.976 1.12 -0.440 #> 2 Alaska 10 263 48 44.5 -1.93 1.06 2.02 #> 3 Arizona 8.1 294 80 31 -1.75 -0.738 0.0542 #> 4 Arkansas 8.8 190 50 19.5 0.140 1.11 0.113 #> 5 California 9 276 91 40.6 -2.50 -1.53 0.593 #> 6 Colorado 7.9 204 78 38.7 -1.50 -0.978 1.08 #> 7 Connecticut 3.3 110 77 11.1 1.34 -1.08 -0.637 #> 8 Delaware 5.9 238 72 15.8 -0.0472 -0.322 -0.711 #> 9 Florida 15.4 335 80 31.9 -2.98 0.0388 -0.571 #> 10 Georgia 17.4 211 60 25.8 -1.62 1.27 -0.339 #> # … with 40 more rows, and 1 more variable: .fittedPC4 <dbl> ggplot(au, aes(.fittedPC1, .fittedPC2)) + geom_point() + geom_text(aes(label = .rownames), vjust = 1, hjust = 1)