step_nnmf creates a specification of a recipe step
that will convert numeric data into one or more non-negative
step_nnmf( recipe, ..., role = "predictor", trained = FALSE, num_comp = 2, num_run = 30, options = list(), res = NULL, prefix = "NNMF", seed = sample.int(10^5, 1), keep_original_cols = FALSE, skip = FALSE, id = rand_id("nnmf") ) # S3 method for step_nnmf 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
For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new 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 components to retain as new
A positive integer for the number of computations runs used to obtain a consensus projection.
A list of options to
A character string that will be the prefix to the resulting new variables. See notes below.
An integer that will be used to set the seed in isolation when computing the factorization.
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
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 columns
selectors or variables selected) and the number of components.
Non-negative matrix factorization computes latent components that have non-negative values and take into account that the original data have non-negative values.
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,
num < 10, their names will be
num = 101, the names would be
library(modeldata) data(biomass) # rec <- recipe(HHV ~ ., data = biomass) %>% # update_role(sample, new_role = "id var") %>% # update_role(dataset, new_role = "split variable") %>% # step_nnmf(all_numeric_predictors(), num_comp = 2, seed = 473, num_run = 2) %>% # 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()