knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
In this vignette, we will demonstrate basic functionalities of Rdimtools package by walking through from installation to analysis with the famous iris dataset.
Rdimtools can be installed in two handy options. A release version from CRAN can be installed
or a development version is available from GitHub with
## install.packages("devtools") devtools::install_github("kisungyou/Rdimtools")
Now we are ready to go by loading the library.
vernow = utils::packageVersion("Rdimtools") ndo = (sum(unlist(lapply(ls("package:Rdimtools"), startsWith, "do.")))) nest = (sum(unlist(lapply(ls("package:Rdimtools"), startsWith, "est."))))
As of current release version
r vernow, there are
r nest intrinsic dimension estimation (IDE) algorithms. In the following example, we will only show 5 methods' performance.
# load the iris data X = as.matrix(iris[,1:4]) lab = as.factor(iris[,5]) # we will compare 5 methods (out of 17 methods from version 1.0.0) vecd = rep(0,5) vecd = est.Ustat(X)$estdim # convergence rate of U-statistic on manifold vecd = est.correlation(X)$estdim # correlation dimension vecd = est.made(X)$estdim # manifold-adaptive dimension estimation vecd = est.mle1(X)$estdim # MLE with Poisson process vecd = est.twonn(X)$estdim # minimal neighborhood information # let's visualize plot(1:5, vecd, type="b", ylim=c(1.5,3.5), main="estimating dimension of iris data", xaxt="n",xlab="",ylab="estimated dimension") xtick = seq(1,5,by=1) axis(side=1, at=xtick, labels = FALSE) text(x=xtick, par("usr"), labels = c("Ustat","correlation","made","mle1","twonn"), pos=1, xpd = TRUE)
As the true dimension is not known for a given dataset, different methods bring about heterogeneous estimates. That's why we deliver 17 methods at an unprecedented scale to provide a broader basis for your decision.
Currently, Rdimtools (ver
r vernow) delivers
r ndo dimension reduction/feature selection/manifold learning algorithms. Among a myriad of methods, we compare Principal Component Analysis (
do.pca), Laplacian Score (
do.lscore), and Diffusion Maps (
do.dm) are compared, each from a family of algorithms for linear reduction, feature extraction, and nonlinear reduction.
# run 3 algorithms mentioned above mypca = do.pca(X, ndim=2) mylap = do.lscore(X, ndim=2) mydfm = do.dm(X, ndim=2, bandwidth=10) # extract embeddings from each method Y1 = mypca$Y Y2 = mylap$Y Y3 = mydfm$Y # visualize par(mfrow=c(1,3)) plot(Y1, pch=19, col=lab, xlab="axis 1", ylab="axis 2", main="PCA") plot(Y2, pch=19, col=lab, xlab="axis 1", ylab="axis 2", main="Laplacian Score") plot(Y3, pch=19, col=lab, xlab="axis 1", ylab="axis 2", main="Diffusion Maps")
As the figure above shows, in general, different algorithms show heterogeneous nature of the data. We hope Rdimtools be a valuable toolset to help practitioners and scientists discover many facets of the data.
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