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# Rdimtools <a href='https://www.kisungyou.com/Rdimtools/'><img src='man/figures/logo.png' align="right" height="139" /></a>

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**Rdimtools** is an R package for dimension reduction (DR) - including
feature selection and manifold learning - and intrinsic dimension
estimation (IDE) methods. We aim at building one of the *most
comprehensive* toolbox available online, where current version delivers
145 DR algorithms and 17 IDE methods.

The philosophy is simple, **the more we have at hands, the better we can
play**.

## Elephant

Our logo characterizes the foundational nature of multivariate data
analysis; we may be blind people wrangling the data to see an
[elephant](https://en.wikipedia.org/wiki/Blind_men_and_an_elephant) to
grasp an idea of what the data looks like with partial information from
each algorithm.

## Installation

You can install a release version from CRAN:

``` r
install.packages("Rdimtools")
```

or the development version from github:

``` r
## install.packages("devtools")
devtools::install_github("kisungyou/Rdimtools")
```

## Minimal Example : Dimension Reduction

Here is an example of dimension reduction on the famous `iris` dataset.
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.

``` r
# load the library
library(Rdimtools)

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

# 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)

# visualize
par(mfrow=c(1,3), pty="s")
plot(mypca$Y, pch=19, col=lab, xlab="PCA1", ylab="PCA2", main="PCA")
plot(mylap$Y, pch=19, col=lab, xlab="LS1", ylab="LS2", main="Laplacian Score")
plot(mydfm$Y, pch=19, col=lab, xlab="DM1", ylab="DM2", main="Diffusion Maps")
```

<img src="man/figures/README-unnamed-chunk-2-1.png" alt="" width="90%" style="display: block; margin: auto;" />

## Minimal Example : Dimension Estimation

![](https://people.cs.uchicago.edu/~dinoj/manifold/swissroll.gif)

Swill Roll is a classic example of 2-dimensional manifold embedded in
$\mathbb{R}^3$ and one of 11 famous model-based samples from
`aux.gensamples()` function. Given the ground truth that $d=2$, let’s
apply several methods for intrinsic dimension estimation.

``` r
# generate sample data
set.seed(100)
roll = aux.gensamples(dname="swiss")

# we will compare 6 methods (out of 17 methods from version 1.0.0)
vecd = rep(0,5)
vecd[1] = est.Ustat(roll)$estdim       # convergence rate of U-statistic on manifold
vecd[2] = est.correlation(roll)$estdim # correlation dimension
vecd[3] = est.made(roll)$estdim        # manifold-adaptive dimension estimation
vecd[4] = est.mle1(roll)$estdim        # MLE with Poisson process
vecd[5] = est.twonn(roll)$estdim       # minimal neighborhood information

# let's visualize
plot(1:5, vecd, type="b", ylim=c(1.5,2.5), 
     main="true dimension is d=2",
     xaxt="n",xlab="",ylab="estimated dimension")
xtick = seq(1,5,by=1)
axis(side=1, at=xtick, labels = FALSE)
text(x=xtick,  par("usr")[3], 
     labels = c("Ustat","correlation","made","mle1","twonn"), pos=1, xpd = TRUE)
```

<img src="man/figures/README-unnamed-chunk-3-1.png" alt="" width="80%" style="display: block; margin: auto;" />

We can observe that all 5 methods we tested estimated the intrinsic
dimension around $d=2$. It should be noted that the estimated dimension
may not be integer-valued due to characteristics of each method.

## Acknowledgements

The logo icon is made by
[Freepik](https://www.flaticon.com/authors/freepik/) from
[www.flaticon.com](https://www.flaticon.com/).The rotating Swiss Roll
image is taken from [Dinoj
Surendran](https://people.cs.uchicago.edu/~dinoj/manifold/swissroll.html)’s
website.
