This package contains functions to compute, print and plot Least Squares Sparse Principal Components Analysis (LS-SPCA). Methodological details, references and full presentation can be found in the extended_vignette document.

Installation

You can install the stable release version from CRAN

install.packages("spca")
#or
https://github.com/merolagio/spca/releases/tag/CRAN_submission

The current development version from GitHub

remotes::install_github("merolagio/spca")

Usage

The main function spca() computes the sparse loadings and various statistics, such as the variance explained by each sparse component (sPC). print, summery and plot methods are available. PCA solutions stored as an *spca* object cn be obtained with the function pca().

Utilities available are compare_spca()(to compare two or more spca solutions), *aggregate_by_scale()* (to visualize the contribution by scale) and *new.spca()* (to create anspca` object from a set of loadings).

Example

Load data

The holzinger dataset is the small classic Holzinger-Swineford dataset with 145 cases on 12 variables grouped in 4 scales.

library(spca)
data(holzinger)
dim(holzinger)
#> [1] 145  12
holzinger_scales
#>  [1] SPL SPL SPL VBL VBL VBL SPD SPD SPD MTH MTH MTH
#> Levels: SPL VBL SPD MTH

Preliminary PCA

ho_pca = pca(holzinger, screeplot =  TRUE, qq_plot = TRUE)
summary(ho_pca,cols = 10)
#>          sPC1   sPC2   sPC3   sPC4   sPC5   sPC6   sPC7   sPC8   sPC9  sPC10
#> Vexp    40.2%  13.7%  10.6%   6.4%   5.6%   5.1%   4.3%   3.9%   3.2%   2.6%
#> Cvexp   40.2%  53.9%  64.5%  70.9%  76.5%  81.6%  85.9%  89.8%  93.0%  95.6%
#> Rvexp  100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
#> Rcvexp 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
#> Card       12     12     12     12     12     12     12     12     12     12

We settle for 4 components

Compute the sparse loadings

Important parameters in the spca() function are: alpha which controls for the minimum