https://github.com/annennenne/pcadsc

An R package for performing Principal Component Analysis-based Data Structure Comparisons (PCADSC)
https://github.com/annennenne/pcadsc

data-structures exploratory-data-visualizations principal-component-analysis r

Last synced: about 2 months ago
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An R package for performing Principal Component Analysis-based Data Structure Comparisons (PCADSC)

• Host: GitHub
• URL: https://github.com/annennenne/pcadsc
• Owner: annennenne
• Created: 2016-11-25T13:12:53.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2022-04-30T14:55:16.000Z (almost 3 years ago)
• Last Synced: 2024-10-16T04:20:26.822Z (4 months ago)
• Topics: data-structures, exploratory-data-visualizations, principal-component-analysis, r
• Language: TeX
• Homepage:
• Size: 15.2 MB
• Stars: 1
• Watchers: 4
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# PCADSC

[![CRAN\_Release\_Badge](http://www.r-pkg.org/badges/version-ago/PCADSC)](https://CRAN.R-project.org/package=PCADSC)

![Download counter](http://cranlogs.r-pkg.org/badges/grand-total/PCADSC)

Development version of the R package PCADSC. The last stable version of the package can 

be installed from CRAN using:

```{r}

install.packages('PCADSC')

```

To install the development version of PCADSC, run the following commands

from within R:

```{r}

library(devtools)

install_github('annennenne/PCADSC')

```

The PCADSC methods are described in our [article manuscript](https://github.com/AnnePetersen1/PCADSC/blob/master/article/PCADSC_manuscript_withFigures.pdf). 

The basic functionality of the package can be inspected using the following lines of code:

```{r}

library(PCADSC)

#Load iris data

data(iris)

#Define grouping variable, grouping the observations by whether their species is

#Setosa or not

iris$group <- "setosa"

iris$group[iris$Species != "setosa"] <- "non-setosa"

#Make a PCADSC object, splitting the data by "group"

irisPCADSC <-PCADSC(iris, "group", var=setdiff(names(iris), c("group", "Species")),

                    doCE = TRUE, doAngle = TRUE, doChroma = TRUE)

                         

#Make a cumulative eigenvalue (CE) plot

CEPlot(irisPCADSC)

#Make an angle plot 

anglePlot(irisPCADSC)

#Make a chroma plot

chromaPlot(irisPCADSC)

```

Our current to-do list for the package is posted below. If you have any further suggestions for added functionality, or have a bug to report, please open an issue and let us know.

**To do**

- Make `print()` methods for `pcaRes`, `CEInfo`, `angleInfo`, `chromaInfo` and `PCASDC` objects

- Make `summary()` methods for `pcaRes`, `CEInfo`, `angleInfo`, `chromaInfo` and `PCASDC` object