Description Usage Arguments Details Value References See Also Examples
step_pca
creates a specification of a recipe step
that will convert numeric data into one or more principal
components.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 
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_comp 
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. 
skip 
A logical. Should the step be skipped when the
recipe is baked by 
id 
A character string that is unique to this step to identify it. 
x 
A 
type 
For the 
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 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.
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
.
Jolliffe, I. T. (2010). Principal Component Analysis. Springer.
step_ica()
step_kpca()
step_isomap()
recipe()
prep.recipe()
bake.recipe()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  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)

Loading required package: dplyr
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: broom
Attaching package: 'recipes'
The following object is masked from 'package:stats':
step
# A tibble: 50 x 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
# A tibble: 2 x 3
terms value component
<chr> <dbl> <chr>
1 all_numeric() NA <NA>
2 3 NA <NA>
# A tibble: 16 x 3
terms value component
<chr> <dbl> <chr>
1 Murder 0.536 PC1
2 Assault 0.583 PC1
3 UrbanPop 0.278 PC1
4 Rape 0.543 PC1
5 Murder 0.418 PC2
6 Assault 0.188 PC2
7 UrbanPop 0.873 PC2
8 Rape 0.167 PC2
9 Murder 0.341 PC3
10 Assault 0.268 PC3
11 UrbanPop 0.378 PC3
12 Rape 0.818 PC3
13 Murder 0.649 PC4
14 Assault 0.743 PC4
15 UrbanPop 0.134 PC4
16 Rape 0.0890 PC4
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