Prediction with Less Overfitting and Robust to Noise

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.

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))