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, ...)A list returned from irlba::irlba().
Arguments passed on to tidy_svd
matrixCharacter specifying which component of the PCA should be tidied.
"u", "samples", or "x": returns information about the map from
the original space into principle components space.
"v", "rotation", or "variables": returns information about the
map from principle components space back into the original space.
"d" or "pcs": returns information about the eigenvalues
will return information about
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:
rowID of the original observation (i.e. rowname from original data).
PCInteger indicating a principle component.
valueThe 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:
rowThe variable labels (colnames) of the data set on which PCA was performed
PCAn integer vector indicating the principal component
valueThe value of the eigenvector (axis score) on the indicated principal component
If matrix is "d" or "pcs", the columns are:
PCAn integer vector indicating the principal component
std.devStandard deviation explained by this PC
percentPercentage of variation explained
cumulativeCumulative percentage of variation explained
A very thin wrapper around tidy_svd().
Other list tidiers:
glance_optim(),
list_tidiers,
tidy_optim(),
tidy_svd(),
tidy_xyz()
Other svd tidiers:
augment.prcomp(),
tidy.prcomp(),
tidy_svd()