README.md

In spls: Sparse Partial Least Squares (SPLS) Regression and Classification

spls: Sparse Partial Least Squares (SPLS) Regression and Classification

The package spls Provides functions for fitting a sparse partial least squares (SPLS) regression and classification (Chun and Keles (2010) ).

Installation

The spls package is on CRAN (The Comprehensive R Archive Network) and the latest release can be easily installed using the command

install.packages("spls")
library(spls)

Building from source

To install the latest stable development version from GitHub, you can pull this repository and install it using

## install.packages("remotes")
remotes::install_github("valentint/spls", build_opts = c("--no-build-vignettes"))

Of course, if you have already installed remotes, you can skip the first line (I have commented it out).

Example

library(spls)
#> Sparse Partial Least Squares (SPLS) Regression and
#> Classification (version 2.3-0)
data(yeast)
yeast$x[1:5, 1:5]
#>       ABF1_YPD    ACE2_YPD      ADR1_YPD  ARG80_YPD   ARG81_YPD
#> 21  -0.2722730  0.21932294  0.9238359567 -0.4755756 -0.10389318
#> 41   0.1691280  0.53831198  0.0097604993 -0.3219534 -0.19750606
#> 71  -0.1388962  0.02636382  0.0877516229 -0.2234093  0.10307741
#> 78  -0.2865169 -0.31409427 -0.0454998435  0.3262217  0.27757502
#> 102 -0.4950561 -0.14827419  0.0002987512 -0.2179458 -0.02539585
yeast$y[1:5, 1:5]
#>   alpha0 alpha7 alpha14 alpha21 alpha28
#> 1  -0.36  -0.42    0.29   -0.14   -0.19
#> 2   1.04   0.19    0.47   -1.03   -0.63
#> 5  -0.30  -0.45    0.75    0.37    0.27
#> 8  -0.46   0.12   -0.06   -0.76   -0.70
#> 9  -1.35  -0.86   -0.22   -0.38   -0.65

## Tuning parameters
set.seed(1)
cv <- cv.spls( yeast$x, yeast$y, eta = seq(0.1,0.9,0.1), K = c(5:10) )
#> eta = 0.1 
#> eta = 0.2 
#> eta = 0.3 
#> eta = 0.4 
#> eta = 0.5 
#> eta = 0.6 
#> eta = 0.7 
#> eta = 0.8 
#> eta = 0.9 
#> 
#> Optimal parameters: eta = 0.6, K = 8

## SPLS fit
f <- spls( yeast$x, yeast$y, eta = cv$eta.opt, K = cv$K.opt )
print(f)
#> 
#> Sparse Partial Least Squares for multivariate responses
#> ----
#> Parameters: eta = 0.6, K = 8, kappa = 0.5
#> PLS algorithm:
#> pls2 for variable selection, simpls for model fitting
#> 
#> SPLS chose 56 variables among 106 variables
#> 
#> Selected variables: 
#> ACE2_YPD ARG80_YPD   ARG81_YPD   ASH1_YPD    AZF1_YPD    
#> BAS1_YPD CBF1_YPD    CHA4_YPD    CRZ1_YPD    FHL1_YPD    
#> FKH1_YPD FKH2_YPD    FZF1_YPD    GAT1_YPD    GAT3_YPD    
#> GCN4_YPD GCR2_YPD    GLN3_YPD    HAA1_YPD    HAP2_YPD    
#> HAP5_YPD HIR1_YPD    HIR2_YPD    IME4_YPD    INO4_YPD    
#> A1..MATA1._YPD   MBP1_YPD    MCM1_YPD    MET4_YPD    MSN2_YPD    
#> NDD1_YPD NRG1_YPD    PHD1_YPD    PHO2_YPD    PUT3_YPD    
#> RCS1_YPD REB1_YPD    RFX1_YPD    RIM101_YPD  RME1_YPD    
#> RTG1_YPD RTG3_YPD    SIP4_YPD    SOK2_YPD    STB1_YPD    
#> STE12_YPD    STP2_YPD    SWI4_YPD    SWI5_YPD    SWI6_YPD    
#> THI2_YPD YAP1_YPD    YAP6_YPD    YAP7_YPD    YFL044C_YPD 
#> YJL206C_YPD  

plot.spls( f, yvar=1 )

coefplot.spls( f, nwin=c(2,2), xvar=c(1:4) )

Community guidelines

Report issues and request features

If you experience any bugs or issues or if you have any suggestions for additional features, please submit an issue via the Issues tab of this repository. Please have a look at existing issues first to see if your problem or feature request has already been discussed.

Contribute to the package

If you want to contribute to the package, you can fork this repository and create a pull request after implementing the desired functionality.

Ask for help

If you need help using the package, or if you are interested in collaborations related to this project, please get in touch with the package maintainer.

spls documentation built on June 30, 2025, 9:07 a.m.