# Non-Negative Matrix Factorization Signal Extraction with lasso Penalization

Source:`R/nnmf_sparse.R`

`step_nnmf_sparse.Rd`

`step_nnmf_sparse()`

creates a *specification* of a recipe step
that will convert numeric data into one or more non-negative
components.

## Usage

```
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")
)
```

## Arguments

- 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

`selections()`

for more details.- 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

`num_comp`

is greater than the number of columns or the number of possible components, a smaller value will be used. If`num_comp = 0`

is set then no transformation is done and selected variables will stay unchanged.- 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

`nmf()`

in the RcppML package. That package has a separate function`setRcppMLthreads()`

that controls the amount of internal parallelization.**Note**that the argument`A`

,`k`

,`L1`

, and`seed`

should not be passed here.- res
A matrix of loadings is stored here, along with the names of the original predictors, once this preprocessing step has been trained by

`prep()`

.- 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

`FALSE`

.- skip
A logical. Should the step be skipped when the recipe is baked by

`bake()`

? While all operations are baked when`prep()`

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.- id
A character string that is unique to this step to identify it.

## Value

An updated version of `recipe`

with the new step added to the
sequence of any existing operations.

## Details

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`

.

## Tidying

When you `tidy()`

this step, a tibble with column
`terms`

(the selectors or variables selected) and the number of
components is returned.

## See also

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()`

## Examples

```
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()
}
```