Description
Prediction with Less Overfitting and Robust to Noise.
Description
A method for the quantitative prediction with much predictors. This package provides functions to construct the quantitative prediction model with less overfitting and robust to noise.
README.md
PLORN
The goal of PLORN is to provide the functions to construct a prediction model of environments using noisy omics data linked with the environments based on PLORN algorithm.
Installation
You can install the development version of PLORN from GitHub with:
#install.packages(“devtools”)
devtools::install_github(“takakoizumi/PLORN”)
Example
install.packages(“PLORN”)
library(PLORN)
# basic example code
data(Pinus)
x.train <- Pinus[[1]]
x.test <- Pinus[[2]]
y <- Pinus[[3]]
cor(y, plorn(x.train, y, newx = x.test, n.pred = 100))