Rdimtools (version 1.1.2)

do.phate: 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,
  ...
)

Value

a named Rdimtools S3 object containing

Y

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

algorithm

name of the algorithm.

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).

References

moon_visualizing_2019Rdimtools

Examples

# \donttest{
## 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)
# }