step_nnmf_sparse {recipes} | R Documentation |
step_nnmf_sparse()
creates a specification of a recipe step
that will convert numeric data into one or more non-negative
components.
step_nnmf_sparse( recipe, ..., role = "predictor", trained = FALSE, num_comp = 2, penalty = 0.001, options = list(), res = NULL, prefix = "NNMF", seed = sample.int(10^5, 1), keep_original_cols = FALSE, skip = FALSE, id = rand_id("nnmf_sparse") )
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 |
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 |
penalty |
A non-negative number used as a penalization factor for the loadings. Values are usually between zero and one. |
options |
A list of options to |
res |
A matrix of loadings is stored here, along with the names of the
original predictors, once this preprocessing step has been trained by
|
prefix |
A character string for the prefix of the resulting new variables. See notes below. |
seed |
An integer that will be used to set the seed in isolation when computing the factorization. |
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. |
Non-negative matrix factorization computes latent components that have non-negative values and take into account that the original data have non-negative values.
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 < 10
, their names will be NNMF1
- NNMF9
. If
num = 101
, the names would be NNMF001
- NNMF101
.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble with column
terms
(the selectors or variables selected) and the number of
components is returned.
The underlying operation does not allow for case weights.
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()
,
step_pca()
,
step_pls()
,
step_ratio()
,
step_spatialsign()
if (rlang::is_installed(c("modeldata", "RcppML", "ggplot2"))) { library(Matrix) data(biomass, package = "modeldata") rec <- recipe(HHV ~ ., data = biomass) %>% update_role(sample, new_role = "id var") %>% update_role(dataset, new_role = "split variable") %>% step_nnmf_sparse( all_numeric_predictors(), num_comp = 2, seed = 473, penalty = 0.01 ) %>% prep(training = biomass) bake(rec, new_data = NULL) library(ggplot2) bake(rec, new_data = NULL) %>% ggplot(aes(x = NNMF2, y = NNMF1, col = HHV)) + geom_point() }