nonlinear_PHATE: Potential of Heat Diffusion for Affinity-based Transition...

In Rdimtools: Dimension Reduction and Estimation Methods

Potential of Heat Diffusion for Affinity-based Transition Embedding

Description

PHATE is a nonlinear method that is specifically targeted at visualizing high-dimensional data by embedding it on 2- or 3-dimensional space. We offer a native implementation of PHATE solely in R/C++ without interface to python module.

Usage

do.phate(
  X,
  ndim = 2,
  k = 5,
  alpha = 10,
  dtype = c("sqrt", "log"),
  smacof = TRUE,
  ...
)

Arguments

X	an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables.
ndim	an integer-valued target dimension (default: 2).
k	size of nearest neighborhood (default: 5).
alpha	decay parameter for Gaussian kernel exponent (default: 10).
dtype	type of potential distance transformation; "log" or "sqrt" (default: "sqrt").
smacof	a logical; TRUE to use SMACOF for Metric MDS or FALSE to use Classical MDS (default: TRUE).
...	extra parameters including maxiter maximum number of iterations (default: 100). abstol absolute stopping criterion for metric MDS iterations (default: 1e-8).

Value

a named Rdimtools S3 object containing

Y

an (n\times ndim) matrix whose rows are embedded observations.

algorithm

name of the algorithm.

References

\insertRef

moon_visualizing_2019Rdimtools

Examples

## load iris data
data(iris)
X     = as.matrix(iris[,1:4])
lab   = as.factor(iris[,5])

## compare different neighborhood sizes.
pca2d <- do.pca(X, ndim=2)
phk01 <- do.phate(X, ndim=2, k=2)
phk02 <- do.phate(X, ndim=2, k=5)
phk03 <- do.phate(X, ndim=2, k=7)

## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2))
plot(pca2d$Y, col=lab, pch=19, main="PCA")
plot(phk01$Y, col=lab, pch=19, main="PHATE:k=2")
plot(phk02$Y, col=lab, pch=19, main="PHATE:k=5")
plot(phk03$Y, col=lab, pch=19, main="PHATE:k=7")
par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.