step_isomap
creates a specification of a recipe
step that will convert numeric data into one or more new
dimensions.
step_isomap( recipe, ..., role = "predictor", trained = FALSE, num_terms = 5, neighbors = 50, options = list(.mute = c("message", "output")), res = NULL, prefix = "Isomap", skip = FALSE, id = rand_id("isomap") ) # S3 method for step_isomap tidy(x, ...)
recipe  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 dimensions. See

role  For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new dimension columns created by 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 
neighbors  The number of neighbors. 
options  A list of options to 
res  The 
prefix  A character string that will be the prefix to the resulting new variables. See notes below. 
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. 
x  A 
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 terms
(the
selectors or variables selected).
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
.
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
# \donttest{ library(modeldata) data(biomass) 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_predictors()) %>% step_normalize(all_predictors()) %>% step_isomap(all_predictors(), neighbors = 100, num_terms = 2) if (require(dimRed) & require(RSpectra)) { 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) }#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#> # A tibble: 5 x 2 #> terms id #> <chr> <chr> #> 1 carbon isomap_u8gfY #> 2 hydrogen isomap_u8gfY #> 3 oxygen isomap_u8gfY #> 4 nitrogen isomap_u8gfY #> 5 sulfur isomap_u8gfY# }