step_isomap()
creates a specification of a recipe step that uses
multidimensional scaling to convert numeric data into one or more new
dimensions.
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_terms
The number of isomap dimensions to retain as new predictors. If
num_terms
is greater than the number of columns or the number of possible dimensions, a smaller value will be used. neighbors
The number of neighbors.
 options
A list of options to
dimRed::Isomap()
. res
The
dimRed::Isomap()
object is stored here once this preprocessing step has be trained byprep()
. columns
A character string of the selected variable names. This field is a placeholder and will be populated once
prep()
is used. prefix
A character string for the prefix of the resulting new variables. See notes below.
 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 whenprep()
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 usingskip = 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
Isomap is a form of multidimensional scaling (MDS). MDS methods try to find a reduced set of dimensions such that the geometric distances between the original data points are preserved. This version of MDS uses nearest neighbors in the data as a method for increasing the fidelity of the new dimensions to the original data values.
This step requires the dimRed, RSpectra, igraph, and RANN packages. If not installed, the step will stop with a note about installing these packages.
It is advisable to center and scale the variables prior to
running Isomap (step_center
and step_scale
can be
used for this purpose).
The argument num_terms
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_terms < 10
, their names will be Isomap1

Isomap9
. If num_terms = 101
, the names would be
Isomap001
 Isomap101
.
Tidying
When you tidy()
this step, a tibble is returned with
columns terms
, and id
:
 terms
character, the selectors or variables selected
 id
character, id of this step
Tuning Parameters
This step has 2 tuning parameters:
num_terms
: # Model Terms (type: integer, default: 5)neighbors
: # Nearest Neighbors (type: integer, default: 50)
References
De Silva, V., and Tenenbaum, J. B. (2003). Global versus local methods in nonlinear dimensionality reduction. Advances in Neural Information Processing Systems. 721728.
dimRed, a framework for dimensionality reduction, https://github.com/gdkrmr
See also
Other multivariate transformation steps:
step_classdist()
,
step_classdist_shrunken()
,
step_depth()
,
step_geodist()
,
step_ica()
,
step_kpca()
,
step_kpca_poly()
,
step_kpca_rbf()
,
step_mutate_at()
,
step_nnmf()
,
step_nnmf_sparse()
,
step_pca()
,
step_pls()
,
step_ratio()
,
step_spatialsign()
Examples
if (FALSE) {
data(biomass, package = "modeldata")
biomass_tr < biomass[biomass$dataset == "Training", ]
biomass_te < biomass[biomass$dataset == "Testing", ]
rec < recipe(
HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
data = biomass_tr
)
im_trans < rec %>%
step_YeoJohnson(all_numeric_predictors()) %>%
step_normalize(all_numeric_predictors()) %>%
step_isomap(all_numeric_predictors(), neighbors = 100, num_terms = 2)
im_estimates < prep(im_trans, training = biomass_tr)
im_te < bake(im_estimates, biomass_te)
rng < extendrange(c(im_te$Isomap1, im_te$Isomap2))
plot(im_te$Isomap1, im_te$Isomap2,
xlim = rng, ylim = rng
)
tidy(im_trans, number = 3)
tidy(im_estimates, number = 3)
}