Tidy a(n) svd object masquerading as list
Broom tidies a number of lists that are effectively S3
objects without a class attribute. For example, stats::optim(),
svd() and akima::interp() produce consistent output, but
because they do not have a class attribute, they cannot be handled by S3
dispatch.
These functions look at the elements of a list and determine if there is
an appropriate tidying method to apply to the list. Those tidiers are
themselves are implemented as functions of the form tidy_<function>
or glance_<function> and are not exported (but they are documented!).
If no appropriate tidying method is found, throws an error.
tidy_svd(x, matrix = "u", ...)
x |
A list with components |
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 |
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.
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:
|
ID of the original observation (i.e. rowname from original data). |
|
Integer indicating a principal component. |
|
The score of the observation for that particular principal component. That is, the location of the observation in PCA space. |
If matrix is "v", "rotation", "loadings" or "variables", each
row in the tidied output corresponds to information about the principle
components in the original space. The columns are:
|
The variable labels (colnames) of the data set on which PCA was performed |
|
An integer vector indicating the principal component |
|
The value of the eigenvector (axis score) on the indicated principal component |
If matrix is "d", "eigenvalues" or "pcs", the columns are:
|
An integer vector indicating the principal component |
|
Standard deviation explained by this PC |
|
Fraction of variation explained by this component (a numeric value between 0 and 1). |
|
Cumulative fraction of variation explained by principle components up to this component (a numeric value between 0 and 1). |
Other svd tidiers:
augment.prcomp(),
tidy.prcomp(),
tidy_irlba()
Other list tidiers:
glance_optim(),
list_tidiers,
tidy_irlba(),
tidy_optim(),
tidy_xyz()
if (requireNamespace("modeldata", quietly = TRUE)) {
library(modeldata)
data(hpc_data)
mat <- scale(as.matrix(hpc_data[, 2:5]))
s <- svd(mat)
tidy_u <- tidy(s, matrix = "u")
tidy_u
tidy_d <- tidy(s, matrix = "d")
tidy_d
tidy_v <- tidy(s, matrix = "v")
tidy_v
library(ggplot2)
library(dplyr)
ggplot(tidy_d, aes(PC, percent)) +
geom_point() +
ylab("% of variance explained")
tidy_u %>%
mutate(class = hpc_data$class[row]) %>%
ggplot(aes(class, value)) +
geom_boxplot() +
facet_wrap(~PC, scale = "free_y")
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