View source: R/pca_sparse_bayes.R
step_pca_sparse_bayes  R Documentation 
step_pca_sparse_bayes()
creates a specification of a recipe step that will convert
numeric data into one or more principal components that can have some zero
coefficients.
step_pca_sparse_bayes( recipe, ..., role = "predictor", trained = FALSE, num_comp = 5, prior_slab_dispersion = 1, prior_mixture_threshold = 0.1, options = list(), res = NULL, prefix = "PC", keep_original_cols = FALSE, skip = FALSE, id = rand_id("pca_sparse_bayes") )
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 
prior_slab_dispersion 
This value is proportional to the dispersion (or scale) parameter for the slab portion of the prior. Smaller values result in an increase in zero coefficients. 
prior_mixture_threshold 
The parameter that defines the tradeoff between the spike and slab components of the prior. Increasing this parameter increases the number of zero coefficients. 
options 
A list of options to the default method for

res 
The rotation matrix once this preprocessing step has been trained
by 
prefix 
A character string that will be the prefix to the resulting new variables. See notes below. 
keep_original_cols 
A logical to keep the original variables in the
output. Defaults to 
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. 
The VBsparsePCA
package is required for this step. If it is not installed,
the user will be prompted to do so when the step is defined.
A spikeandslab prior is a mixture of two priors. One (the "spike") has all of its mass at zero and represents a variable that has no contribution to the PCA coefficients. The other prior is a broader distribution that reflects the coefficient distribution of variables that do affect the PCA analysis. This is the "slab". The narrower the slab, the more likely that a coefficient will be zero (or are regularized to be closer to zero). The mixture of these two priors is governed by a mixing parameter, which itself has a prior distribution and a hyperparameter prior.
PCA coefficients and their resulting scores are unique only up to the sign. This step will attempt to make the sign of the components more consistent from runtorun. However, the sparsity constraint may interfere with this goal.
The argument num_comp
controls the number of components that
will be retained (per default 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
.
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
.
When you tidy()
this step, a tibble with columns
terms
(the selectors or variables selected), value
and component
is
returned.
The underlying operation does not allow for case weights.
Ning, B. (2021). Spike and slab Bayesian sparse principal component analysis. arXiv:2102.00305.
step_pca_sparse()
library(recipes) library(ggplot2) data(ad_data, package = "modeldata") ad_rec < recipe(Class ~ ., data = ad_data) %>% step_zv(all_predictors()) %>% step_YeoJohnson(all_numeric_predictors()) %>% step_normalize(all_numeric_predictors()) %>% step_pca_sparse_bayes( all_numeric_predictors(), prior_mixture_threshold = 0.95, prior_slab_dispersion = 0.05, num_comp = 3, id = "sparse bayesian pca" ) %>% prep() tidy(ad_rec, id = "sparse bayesian pca") %>% mutate(value = ifelse(value == 0, NA, value)) %>% ggplot(aes(x = component, y = terms, fill = value)) + geom_tile() + scale_fill_gradient2() + theme(axis.text.y = element_blank())
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