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
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") ) # S3 method for step_pca_sparse_bayes 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.
num_comp is greater than the number of columns or the number of
possible components, a smaller value will be used. A value of zero indicates
that PCA will not be used on the data.
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.
The parameter that defines the trade-off between the spike and slab components of the prior. Increasing this parameter increases the number of zero coefficients.
A list of options to the default method for
The rotation matrix once this preprocessing step has been trained
A character string that will be the prefix to the resulting new variables. See notes below.
A logical to keep the original variables in the
output. Defaults to
A logical. Should the step be skipped when the
recipe is baked by
recipes::bake.recipe()? While all operations are baked
recipes::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.
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
terms (the selectors or variables selected),
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 spike-and-slab 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 hyper-parameter 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 run-to-run. However, the sparsity constraint may interfere with this goal.
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,
num_comp < 10, their names will be
num_comp = 101, the names would be
Ning, B. (2021). Spike and slab Bayesian sparse principal component analysis. arXiv:2102.00305.
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())