Wrappers of various dimension reduction algorithms. Data after dimension reduction can also be easily visualized.
Algorithms that can be used in dr package are Principal Components Analysis, Kernel Principal Components Analysis, Classical Multidimensional Scaling, t-Distributed Stochastic Neighbor Embedding, Locally linear embedding, Self-Organizing Map and Diffusion Map.
You can install from R console.
If devtools is not installed on your PC, install devtools with Internet connection.
install.packages("devtools")
Install from GitHub using devtools.
library(devtools)
install_github("ToshihiroIguchi/dr")
Load the dr package and attach it.
library(dr)
We show a concrete example of dr package in iris.
Like the lm function, it can be written in formula form like objective variable and explanatory variable.
In the dr package, you can analyze it without including the object variable.
Both numerical values and factors can be entered for object variables and explanatory variables.
result <- dr(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris)
Dimension reduction is performed by inputting the above command. The result can be confirmed with the plot function.
plot(result)
For reference, only the result of Principal Components Analysis is shown.
MIT License
Copyright (c) 2018 Toshihiro Iguchi
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SOFTWARE.
Toshihiro Iguchi