tidy_irlba {broom} | R Documentation |
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_irlba(x, ...)
x |
A list returned from |
... |
Arguments passed on to
|
A very thin wrapper around tidy_svd()
.
A tibble::tibble with columns depending on the component of PCA being tidied.
If matrix
is "u"
, "samples"
, 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 principle component. |
|
The score of the observation for that particular principle component. That is, the location of the observation in PCA space. |
If matrix
is "v"
, "rotation"
, or "variables"
, each row in the
tidied ouput 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"
or "pcs"
, the columns are:
|
An integer vector indicating the principal component |
|
Standard deviation explained by this PC |
|
Percentage of variation explained |
|
Cumulative percentage of variation explained |
Other list tidiers: glance_optim
,
list_tidiers
, tidy_optim
,
tidy_svd
, tidy_xyz
Other svd tidiers: augment.prcomp
,
tidy.prcomp
, tidy_svd