https://github.com/fbertran/plsRcox
plsRcox R package
https://github.com/fbertran/plsRcox
Last synced: about 2 months ago
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plsRcox R package
- Host: GitHub
- URL: https://github.com/fbertran/plsRcox
- Owner: fbertran
- Created: 2014-04-04T22:07:17.000Z (almost 12 years ago)
- Default Branch: master
- Last Pushed: 2025-09-13T22:37:54.000Z (4 months ago)
- Last Synced: 2025-10-31T08:48:23.135Z (3 months ago)
- Language: R
- Size: 7.43 MB
- Stars: 6
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
- Road2R - plsRcox - Partial Least Squares Regression for Cox Models and Related Techniques. (Table of Contents / Bioinformatics)
README
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
dpi=300,fig.width=7,
fig.keep="all"
)
```
# plsRcox 
# plsRcox, Cox-Models in a High Dimensional Setting in R
## Frédéric Bertrand and Myriam Maumy-Bertrand
[](https://doi.org/10.32614/CRAN.package.plsRcox)
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://www.repostatus.org/#active)
[](https://github.com/fbertran/plsRcox/actions)
[](https://app.codecov.io/gh/fbertran/plsRcox?branch=master)
[](https://cran.r-project.org/package=plsRcox)
[](https://cran.r-project.org/package=plsRcox)
[](https://github.com/fbertran/plsRcox)
The goal of plsRcox is provide Cox models in a high dimensional setting in R.
plsRcox implements partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings , Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in [](https://arxiv.org/abs/1810.01005), Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.
The package was presented at the [User2014!](https://user2014.r-project.org/) conference. Frédéric Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Bertrand (2014). "plsRcox, Cox-Models in a high dimensional setting in R", *book of abstracts*, User2014!, Los Angeles, page 177,
.
The plsRcox package contains an original allelotyping dataset from "Allelotyping identification of genomic alterations in rectal chromosomally unstable tumors without preoperative treatment", Benoît Romain, Agnès Neuville, Nicolas Meyer, Cécile Brigand, Serge Rohr, Anne Schneider, Marie-Pierre Gaub and Dominique Guenot (2010), *BMC Cancer*, **10**:561, .
Support for parallel computation and GPU is being developped.
The package provides several modelling techniques related to penalized Cox models or extensions of partial least squares to Cox models. The first two were new algorithms.
- **coxsplsDR** and **cv.coxsplsDR** (Philippe Bastien, Frederic Bertrand, Nicolas Meyer, and Myriam Maumy-Bertrand (2015), "Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data", *Bioinformatics*, **31**(3):397-404, ),
- **coxDKsplsDR** and **cv.coxDKsplsDR** (Philippe Bastien, Frederic Bertrand, Nicolas Meyer, and Myriam Maumy-Bertrand (2015), "Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data", *Bioinformatics*, **31**(3):397-404, ),
- **coxDKplsDR** and **cv.coxDKplsDR** (Philippe Bastien (2008), "Deviance residuals based PLS regression for censored data in high dimensional setting", *Chemometrics and Intelligent Laboratory Systems*, **91**:78–86, ),
- **coxpls** and **cv.coxpls** (Nguyen, D.V., Rocke, D.M. (2002), "Partial least squares proportional hazard regression for application to DNA microarray survival data", *Bioinformatics*, **18**(12):1625–1632),
- **coxplsDR** and **cv.coxplsDR** (Philippe Bastien (2008), "Deviance residuals based PLS regression for censored data in high dimensional setting", *Chemometrics and Intelligent Laboratory Systems*, **91**:78–86, ),
- **DKplsRcox**,
- **larsDR** and **cv.larsDR** (Segal, M.R. (2006), "Microarray Gene Expression Data with Linked Survival Phenotypes: Diffuse large-B- Cell Lymphoma Revisited", *Biostatistics*, **7**:268-285, ),
- **plsRcox** and **cv.plsRcox** (Philippe Bastien, Vincenzo Esposito Vinzi, and Michel Tenenhaus (2005), "PLS generalised linear regression", *Computational Statistics & Data Analysis*, **48**(1):17–46, ),
- **autoplsRcox** and **cv.autoplsRcox** (Philippe Bastien, Vincenzo Esposito Vinzi, and Michel Tenenhaus (2005), "PLS generalised linear regression", *Computational Statistics & Data Analysis*, **48**(1):17–46, ),
This website and these examples were created by F. Bertrand and M. Maumy-Bertrand.
## Installation
You can install the released version of plsRcox from [CRAN](https://CRAN.R-project.org) with:
```{r, eval = FALSE}
install.packages("plsRcox")
```
You can install the development version of plsRcox from [github](https://github.com) with:
```{r, eval = FALSE}
devtools::install_github("fbertran/plsRcox")
```
## Example
### The original allelotyping dataset
```{r microcensure}
library(plsRcox)
data(micro.censure)
Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]
Y_test_micro <- micro.censure$survyear[81:117]
C_test_micro <- micro.censure$DC[81:117]
data(Xmicro.censure_compl_imp)
X_train_micro <- apply((as.matrix(Xmicro.censure_compl_imp)),FUN="as.numeric",MARGIN=2)[1:80,]
X_train_micro_df <- data.frame(X_train_micro)
```
Compute deviance residuals with some options.
```{r devianceresiduals, cache=FALSE}
DR_coxph(Y_train_micro,C_train_micro,plot=TRUE)
```
```{r devianceresiduals2, cache=FALSE}
DR_coxph(Y_train_micro,C_train_micro,scaleY=FALSE,plot=TRUE)
```
```{r devianceresiduals3, cache=FALSE}
DR_coxph(Y_train_micro,C_train_micro,scaleY=TRUE,plot=TRUE)
```
### coxsplsDR
```{r coxsplsDR, cache=TRUE}
(cox_splsDR_fit=coxsplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,eta=.5))
(cox_splsDR_fit2=coxsplsDR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,eta=.5,trace=TRUE))
(cox_splsDR_fit3=coxsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
dataXplan=X_train_micro_df,eta=.5))
rm(cox_splsDR_fit,cox_splsDR_fit2,cox_splsDR_fit3)
```
### cv.coxsplsDR
```{r cvcoxsplsDR, warning=FALSE, cache=TRUE}
set.seed(123456)
(cv.coxsplsDR.res=cv.coxsplsDR(list(x=X_train_micro,time=Y_train_micro, status=C_train_micro),nt=10,eta=.1))
```
### coxDKsplsDR
```{r coxDKsplsDR, cache=TRUE}
(cox_DKsplsDR_fit=coxDKsplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6, validation="CV",eta=.5))
(cox_DKsplsDR_fit=coxDKsplsDR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6, validation="CV",eta=.5))
(cox_DKsplsDR_fit=coxDKsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",dataXplan=data.frame(X_train_micro),eta=.5))
rm(cox_DKsplsDR_fit)
```
### cv.coxsplsDR
```{r cvcoxDKsplsDR, warning=FALSE, cache=TRUE}
set.seed(123456)
(cv.coxDKsplsDR.res=cv.coxDKsplsDR(list(x=X_train_micro,time=Y_train_micro, status=C_train_micro),nt=10,eta=.1))
```
## plsRcox
```{r plsRcox, cache=TRUE}
plsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)
plsRcox(~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)
plsRcox(Xplan=X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE, alpha.pvals.expli=.15)
plsRcox(Xplan=~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE, alpha.pvals.expli=.15)
```
### cv.plsRcox
```{r cvplsRcox, warning=FALSE, cache=TRUE}
set.seed(123456)
(cv.plsRcox.res=cv.plsRcox(list(x=X_train_micro,time=Y_train_micro,status=C_train_micro),nt=10,verbose = FALSE))
```
## DKplsRcox
```{r DKplsRcox, cache=TRUE}
DKplsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)
DKplsRcox(~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)
DKplsRcox(Xplan=X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE, alpha.pvals.expli=.15)
DKplsRcox(Xplan=~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE, alpha.pvals.expli=.15)
```