step_isomap {recipes} | R Documentation |
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 = 5, options = list(knn = 50, .mute = c("message", "output")), res = NULL, prefix = "Isomap")
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 |
The number of isomap dimensions to retain as new predictors. If
|
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 |
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.
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
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
Isomap1
- Isomap9
. If num = 101
, the names would be
Isomap001
- Isomap101
.
An updated version of recipe
with the
new step added to the sequence of existing steps (if any).
De Silva, V., and Tenenbaum, J. B. (2003). Global versus local methods in nonlinear dimensionality reduction. Advances in Neural Information Processing Systems. 721-728.
dimRed, a framework for dimensionality reduction, https://github.com/gdkrmr
step_pca
step_kpca
step_ica
recipe
prep.recipe
bake.recipe
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_center(all_predictors()) %>% step_scale(all_predictors()) %>% step_isomap(all_predictors(), options = list(knn = 100), num = 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)