https://github.com/acsala/sPLSPM

sPLSPM package
https://github.com/acsala/sPLSPM

Last synced: 28 days ago
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sPLSPM package

• Host: GitHub
• URL: https://github.com/acsala/sPLSPM
• Owner: acsala
• Created: 2021-01-28T12:35:12.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-03-25T09:36:46.000Z (over 3 years ago)
• Last Synced: 2024-02-25T04:34:32.293Z (4 months ago)
• Language: R
• Size: 477 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Lists

• awesome-multi-omics - msPLS - Csala - multiset sparse partial least squares path modeling - [paper](https://doi.org/10.1186/s12859-019-3286-3) (Software packages and methods / Multi-omics correlation or factor analysis)

README

        
# sPLSPM R package

[Multiset sparse partial least squares path modeling for high dimensional data analysis](https://doi.org/10.1186/s12859-019-3286-3)

This R application is based on [plspm](https://cran.r-project.org/web/packages/plspm/index.html)

## Installation

Install the development version of [sPLSPM](https://github.com/acsala/sPLSPM)

```r

# install "devtools"

install.packages("devtools") 

library(devtools)

# install "sPLSPM"

install_github("acsala/sPLSPM")

```

## Minimum example

library(sPLSPM)

```r

Generated_data <- sPLSPM::generate_data(number_of_ksi = 1,

                                        number_of_patients = 150,

                                        number_of_Xs_associated_with_ksis = c(15),

                                        number_of_not_associated_Xs = 100,

                                        mean_of_the_regression_weights_of_the_associated_Xs = c(0.9),

                                        sd_of_the_regression_weights_of_the_associated_Xs = c(0.05),

                                        Xnoise_min = -0.2, Xnoise_max = 0.2,

                                        number_of_Ys_associated_with_ksis = c(15),

                                        number_of_not_associated_Ys = 85,

                                        mean_of_the_regression_weights_of_the_associated_Ys = c(0.9),

                                        sd_of_the_regression_weights_of_the_associated_Ys = c(0.05),

                                        Ynoise_min = -0.2, Ynoise_max = 0.2)

X <- Generated_data$X

Y <- Generated_data$Y

data_info1 <- Generated_data$data_info

Data <- cbind(X,Y)

dim(X)

dim(Y)

dim(Data)

## 2 DATASETS connectivity matrix

EXPL_X = c(0,0)

RESP_Y = c(1,0)

path_matrix = rbind(EXPL_X, RESP_Y)

## blocks of outer model

blocks = list(1:dim(X)[2], dim(X)[2]+1:dim(Y)[2])

dim(Data[,blocks[[1]]])

dim(Data[,blocks[[2]]])

## define vector of reflective modes

modes = c("B","A")

## fit the model

time_data <- system.time(

  s_satpls <- splspm(Data, path_matrix, blocks, modes, scheme="path",

                     scaled=T, penalization = "enet", nonzero = c(40,20),

                     cross_validate = T, lambda = 1, maxiter = 100)

)

s_satpls$outer_model

s_satpls$model$iter

s_satpls$nonzero

s_satpls$lambda

```