UMAP-class {dimRed} | R Documentation |

## Umap embedding

### Description

An S4 Class implementing the UMAP algorithm

### Details

Uniform Manifold Approximation is a gradient descend based algorithm that gives results similar to t-SNE, but scales better with the number of points.

### Slots

`fun`

A function that does the embedding and returns a dimRedResult object.

`stdpars`

The standard parameters for the function.

### General usage

Dimensionality reduction methods are S4 Classes that either be used
directly, in which case they have to be initialized and a full
list with parameters has to be handed to the `@fun()`

slot, or the method name be passed to the embed function and
parameters can be given to the `...`

, in which case
missing parameters will be replaced by the ones in the
`@stdpars`

.

### Parameters

UMAP can take the follwing parameters:

- ndim
The number of embedding dimensions.

- knn
The number of neighbors to be used.

- d
The distance metric to use.

- method
`"naive"`

for an R implementation,`"python"`

for the reference implementation.

Other method parameters can also be passed, see
`umap.defaults`

for details. The ones above have been
standardized for the use with `dimRed`

and will get automatically
translated for `umap`

.

### Implementation

The dimRed package wraps the `umap`

packages which provides
an implementation in pure R and also a wrapper around the original python
package `umap-learn`

(https://github.com/lmcinnes/umap/). This requires
`umap-learn`

version 0.4 installed, at the time of writing, there is
already `umap-learn`

0.5 but it is not supported by the R package
`umap`

.

The `"naive"`

implementation is a pure R implementation and considered
experimental at the point of writing this, it is also much slower than the
python implementation.

The `"python"`

implementation is the reference implementation used by
McInees et. al. (2018). It requires the `reticulate`

package for the interaction with python and the python package
`umap-learn`

installed (use `pip install umap-learn`

).

### References

McInnes, Leland, and John Healy. "UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction." https://arxiv.org/abs/1802.03426

### See Also

Other dimensionality reduction methods:
`AutoEncoder-class`

,
`DRR-class`

,
`DiffusionMaps-class`

,
`DrL-class`

,
`FastICA-class`

,
`FruchtermanReingold-class`

,
`HLLE-class`

,
`Isomap-class`

,
`KamadaKawai-class`

,
`MDS-class`

,
`NNMF-class`

,
`PCA-class`

,
`PCA_L1-class`

,
`dimRedMethod-class`

,
`dimRedMethodList()`

,
`kPCA-class`

,
`nMDS-class`

,
`tSNE-class`

### Examples

```
## Not run:
dat <- loadDataSet("3D S Curve", n = 300)
emb <- embed(dat, "UMAP", .mute = NULL, knn = 10)
plot(emb, type = "2vars")
## End(Not run)
```

*dimRed*version 0.2.6 Index]