dimensio

Simple Principal Components Analysis (PCA) and Correspondence Analysis(CA) based on the Singular Value Decomposition (SVD). This packageprovides S4 classes and methods to compute, extract, summarize andvisualize results of multivariate data analysis. It also includesmethods for partial bootstrap validation.

There are many very good packages for multivariate data analysis (suchas FactoMineR,ade4 orca, all extended byFactoExtra). dimensiois designed to be as simple as possible, providing all the necessarytools to explore the results of the analysis.

Installation

You can install the released version of dimensio fromCRAN with:

rinstall.packages("dimensio")

And the development version from GitHub with:

``` r

install.packages("remotes")

remotes::install_github("tesselle/dimensio")```

Usage

``` r

Load packages

library(dimensio)

library(ggplot2)library(ggrepel)library(khroma)```

``` r

Load data

data(iris)

Compute PCA

(non numeric variables are automatically removed)

X <- pca(iris, center = TRUE, scale = TRUE)

> 1 qualitative variable was removed: Species.

```

Summarize

``` r

Summarize results for the individuals (first two components)

summary(X, margin = 1, rank = 2)

> --- Principal Components Analysis (PCA) -----------------------------------------

>

> Eigenvalues:

> eigenvalues variance cumulative

> F1 2.918 73.342 73.342

> F2 0.914 22.970 96.312

> F3 0.147 3.688 100.000

>

> Active individuals:

> dist PC1coord PC1contrib PC1cos2 PC2coord PC2contrib PC2cos2

> 1 2.319 -2.265 1.172 0.954 -0.480 0.168 0.043

> 2 2.202 -2.081 0.989 0.893 0.674 0.331 0.094

> 3 2.389 -2.364 1.277 0.979 0.342 0.085 0.020

> 4 2.378 -2.299 1.208 0.935 0.597 0.260 0.063

> 5 2.476 -2.390 1.305 0.932 -0.647 0.305 0.068

> 6 2.555 -2.076 0.984 0.660 -1.489 1.617 0.340

> 7 2.468 -2.444 1.364 0.981 -0.048 0.002 0.000

> 8 2.246 -2.233 1.139 0.988 -0.223 0.036 0.010

> 9 2.592 -2.335 1.245 0.812 1.115 0.907 0.185

> 10 2.249 -2.184 1.090 0.943 0.469 0.160 0.043

> (150 more)

```

Extract

dimesion provides several methods to extract the results:

• get_data() returns the original data.
• get_contributions() returns the contributions to the definition ofthe principal dimensions.
• get_coordinates() returns the principal coordinates.
• get_correlations() returns the correlations between variables anddimensions.
• get_cos2() returns the cos² values (i.e. the quality ofthe representation of the points on the factor map).
• get_eigenvalues() returns the eigenvalues, the percentages ofvariance and the cumulative percentages of variance.

``` r

Eigenvalues

get_eigenvalues(X)

> eigenvalues variance cumulative

> F1 2.9184978 73.342264 73.34226

> F2 0.9140305 22.969715 96.31198

> F3 0.1467569 3.688021 100.00000

```

Visualize

dimensio uses ggplot2for plotting informations. Visualization methods produce graphics withas few elements as possible: this makes it easy to customize diagrams(e.g. using extra layers, themes and scales).

``` r

Plot active individuals by group

plotrows(X, group = iris$Species) + ggplot2::statellipse() + # Add ellipses ggplot2::themebw() + # Change theme khroma::scalecolor_contrast() # Custom color scale

Plot all individuals by cos2

plotrows(X, highlight = "cos2") + ggplot2::themebw() + # Change theme ggplot2::scalesizecontinuous(range = c(1, 3)) + # Custom size scale khroma::scalecoloriridescent() # Custom color scale```

``` r

Plot variables factor map

plotcolumns(X) + ggrepel::geomlabelrepel() + # Add repelling labels ggplot2::themebw() # Change theme

Highlight contributions

plotcolumns(X, highlight = "contrib") + ggrepel::geomlabelrepel() + # Add repelling labels ggplot2::themebw() + # Change theme khroma::scalecolorYlOrBr(range = c(0.5, 1)) # Custom color scale```

``` r

Plot eigenvalues

plotvariance(X, variance = FALSE, cumulative = FALSE) + ggplot2::themebw() # Change theme

Plot percentages of variance

plotvariance(X, variance = TRUE, cumulative = TRUE) + ggplot2::geomtext(nudgey = 3) + # Add labels ggplot2::themebw() # Change theme

Plot variables contributions to the definition of the first component

plotcontributions(X, margin = 2, axes = 1) + ggplot2::geomtext(nudgey = 2) + # Add labels ggplot2::themebw() + # Change theme ggplot2::theme( # Edit theme # Rotate x axis labels axis.text.x = ggplot2::element_text(angle = 45, hjust = 1, vjust = 1) )

Plot cos2

plotcos2(X, margin = 2, axes = c(1, 2)) + ggplot2::geomtext(nudgey = 0.05) + # Add labels ggplot2::themebw() + # Change theme ggplot2::theme( # Edit theme # Rotate x axis labels axis.text.x = ggplot2::element_text(angle = 45, hjust = 1, vjust = 1) )```

Validation

``` r

Partial bootstrap

Y <- bootstrap(X, n = 30)

Plot with ellipses

plotcolumns(Y) + ggplot2::statellipse() + # Add ellipses ggplot2::theme_bw() # Change theme```

Contributing

Please note that the dimensio project is released with aContributor Code of Conduct. Bycontributing to this project, you agree to abide by its terms.

wget https://archive.org/download/github.com-tesselle-dimensio_-_2021-05-24_16-00-49/tesselle-dimensio_-_2021-05-24_16-00-49.bundle

 git clone tesselle-dimensio_-_2021-05-24_16-00-49.bundle 

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