step_pca
creates a specification of a recipe step that will convert
numeric data into one or more principal components.
Arguments
 recipe
A recipe object. The step will be added to the sequence of operations for this recipe.
 ...
One or more selector functions to choose variables for this step. See
selections()
for more details. role
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from 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_comp
The number of 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. Ifnum_comp = 0
is set then no transformation is done and selected variables will stay unchanged. threshold
A fraction of the total variance that should be covered by the components. For example,
threshold = .75
means thatstep_pca
should generate enough components to capture 75 percent of the variability in the variables. Note: using this argument will override and reset any value given tonum_comp
. options
A list of options to the default method for
stats::prcomp()
. Argument defaults are set toretx = FALSE
,center = FALSE
,scale. = FALSE
, andtol = NULL
. Note that the argumentx
should not be passed here (or at all). res
The
stats::prcomp.default()
object is stored here once this preprocessing step has be trained byprep()
. columns
A character string of variable names that will be populated elsewhere.
 prefix
A character string for the prefix of the resulting new variables. See notes below.
 keep_original_cols
A logical to keep the original variables in the output. Defaults to
FALSE
. skip
A logical. Should the step be skipped when the recipe is baked by
bake()
? While all operations are baked whenprep()
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 usingskip = TRUE
as it may affect the computations for subsequent operations. id
A character string that is unique to this step to identify it.
Value
An updated version of recipe
with the new step added to the
sequence of any existing operations.
Details
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 intervariables correlations in a data set.
It is advisable to standardize 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.
Tidying
When you tidy()
this step, use either type = "coef"
for the variable loadings per component or type = "variance"
for how
much variance each component accounts for.
Case weights
This step performs an unsupervised operation that can utilize case weights.
As a result, case weights are only used with frequency weights. For more
information, see the documentation in case_weights and the examples on
tidymodels.org
.
See also
Other multivariate transformation steps:
step_classdist()
,
step_depth()
,
step_geodist()
,
step_ica()
,
step_isomap()
,
step_kpca_poly()
,
step_kpca_rbf()
,
step_kpca()
,
step_mutate_at()
,
step_nnmf_sparse()
,
step_nnmf()
,
step_pls()
,
step_ratio()
,
step_spatialsign()
Examples
rec < recipe(~., data = USArrests)
pca_trans < rec %>%
step_normalize(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_normalize(all_numeric()) %>%
step_pca(all_numeric(), threshold = .99)
with_thresh < prep(with_thresh, training = USArrests)
bake(with_thresh, USArrests)
#> # A tibble: 50 × 4
#> PC1 PC2 PC3 PC4
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0.976 1.12 0.440 0.155
#> 2 1.93 1.06 2.02 0.434
#> 3 1.75 0.738 0.0542 0.826
#> 4 0.140 1.11 0.113 0.181
#> 5 2.50 1.53 0.593 0.339
#> 6 1.50 0.978 1.08 0.00145
#> 7 1.34 1.08 0.637 0.117
#> 8 0.0472 0.322 0.711 0.873
#> 9 2.98 0.0388 0.571 0.0953
#> 10 1.62 1.27 0.339 1.07
#> # … with 40 more rows
tidy(pca_trans, number = 2)
#> # A tibble: 1 × 4
#> terms value component id
#> <chr> <dbl> <chr> <chr>
#> 1 all_numeric() NA NA pca_Esd0U
tidy(pca_estimates, number = 2)
#> # A tibble: 16 × 4
#> terms value component id
#> <chr> <dbl> <chr> <chr>
#> 1 Murder 0.536 PC1 pca_Esd0U
#> 2 Assault 0.583 PC1 pca_Esd0U
#> 3 UrbanPop 0.278 PC1 pca_Esd0U
#> 4 Rape 0.543 PC1 pca_Esd0U
#> 5 Murder 0.418 PC2 pca_Esd0U
#> 6 Assault 0.188 PC2 pca_Esd0U
#> 7 UrbanPop 0.873 PC2 pca_Esd0U
#> 8 Rape 0.167 PC2 pca_Esd0U
#> 9 Murder 0.341 PC3 pca_Esd0U
#> 10 Assault 0.268 PC3 pca_Esd0U
#> 11 UrbanPop 0.378 PC3 pca_Esd0U
#> 12 Rape 0.818 PC3 pca_Esd0U
#> 13 Murder 0.649 PC4 pca_Esd0U
#> 14 Assault 0.743 PC4 pca_Esd0U
#> 15 UrbanPop 0.134 PC4 pca_Esd0U
#> 16 Rape 0.0890 PC4 pca_Esd0U