step_pca {recipes} | R Documentation |
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 = 5, threshold = NA, options = list(), res = NULL, prefix = "PC")
recipe |
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 |
role |
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. |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
num |
The number of PCA components to retain as new predictors. If
|
threshold |
A fraction of the total variance that should be covered
by the components. For example, |
options |
A list of options to the default method for
|
res |
The |
prefix |
A character string that will be the prefix to the resulting new variables. See notes below |
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
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 < 10
, their names will be
PC1
- PC9
. If num = 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.
An updated version of recipe
with the
new step added to the sequence of existing steps (if any).
Jolliffe, I. T. (2010). Principal Component Analysis. Springer.
step_ica
step_kpca
step_isomap
recipe
prep.recipe
bake.recipe
rec <- recipe( ~ ., data = USArrests) pca_trans <- rec %>% step_center(all_numeric()) %>% step_scale(all_numeric()) %>% step_pca(all_numeric(), num = 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)