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step_pca
creates a specification of a recipe step
that will convert numeric data into one or more principal
components.
step_pca(
recipe,
...,
role = "predictor",
trained = FALSE,
num_comp = 5,
threshold = NA,
options = list(),
res = NULL,
prefix = "PC",
skip = FALSE,
id = rand_id("pca")
)# S3 method for step_pca
tidy(x, ...)
A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose which
variables will be used to compute the components. See
selections()
for more details. For the tidy
method, these are not currently used.
For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new principal component columns created by the original variables will be used as predictors in a model.
A logical to indicate if the quantities for preprocessing have been estimated.
The number of PCA components to retain as new
predictors. If num_comp
is greater than the number of columns
or the number of possible components, a smaller value will be
used.
A fraction of the total variance that should
be covered by the components. For example, threshold = .75
means that step_pca
should generate enough
components to capture 75\
Note: using this argument will override and resent any value
given to num_comp
.
A list of options to the default method for
stats::prcomp()
. Argument defaults are set to
retx = FALSE
, center = FALSE
, scale. = FALSE
, and tol = NULL
. Note that the argument
x
should not be passed here (or at all).
The stats::prcomp.default()
object is
stored here once this preprocessing step has be trained by
prep.recipe()
.
A character string that will be the prefix to the resulting new variables. See notes below
A logical. Should the step be skipped when the
recipe is baked by bake.recipe()
? While all operations are baked
when prep.recipe()
is run, some operations may not be able to be
conducted on new data (e.g. processing the outcome variable(s)).
Care should be taken when using skip = TRUE
as it may affect
the computations for subsequent operations
A character string that is unique to this step to identify it.
A step_pca
object.
An updated version of recipe
with the new step
added to the sequence of existing steps (if any). For the
tidy
method, a tibble with columns terms
(the
selectors or variables selected), value
(the
loading), and component
.
Principal component analysis (PCA) is a transformation of a group of variables that produces a new set of artificial features or components. These components are designed to capture the maximum amount of information (i.e. variance) in the original variables. Also, the components are statistically independent from one another. This means that they can be used to combat large inter-variables correlations in a data set.
It is advisable to standardized the variables prior to running
PCA. Here, each variable will be centered and scaled prior to
the PCA calculation. This can be changed using the
options
argument or by using step_center()
and step_scale()
.
The argument num_comp
controls the number of components that
will be retained (the original variables that are used to derive
the components are removed from the data). The new components
will have names that begin with prefix
and a sequence of
numbers. The variable names are padded with zeros. For example,
if num_comp < 10
, their names will be PC1
- PC9
.
If num_comp = 101
, the names would be PC001
-
PC101
.
Alternatively, threshold
can be used to determine the
number of components that are required to capture a specified
fraction of the total variance in the variables.
Jolliffe, I. T. (2010). Principal Component Analysis. Springer.
step_ica()
step_kpca()
step_isomap()
recipe()
prep.recipe()
bake.recipe()
# NOT RUN {
rec <- recipe( ~ ., data = USArrests)
pca_trans <- rec %>%
step_center(all_numeric()) %>%
step_scale(all_numeric()) %>%
step_pca(all_numeric(), num_comp = 3)
pca_estimates <- prep(pca_trans, training = USArrests)
pca_data <- bake(pca_estimates, USArrests)
rng <- extendrange(c(pca_data$PC1, pca_data$PC2))
plot(pca_data$PC1, pca_data$PC2,
xlim = rng, ylim = rng)
with_thresh <- rec %>%
step_center(all_numeric()) %>%
step_scale(all_numeric()) %>%
step_pca(all_numeric(), threshold = .99)
with_thresh <- prep(with_thresh, training = USArrests)
bake(with_thresh, USArrests)
tidy(pca_trans, number = 3)
tidy(pca_estimates, number = 3)
# }
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