| 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()