WSL/SLF GitLab Repository

apply.model.Rd 1.04 KB
 Adrien Michel committed Nov 11, 2021 1 2 3 4 5 6 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/quantile_application.R \name{apply.model} \alias{apply.model} \title{Apply the trained QM model to a given dataset} \usage{  Adrien Michel committed Nov 11, 2021 7 apply.model(in.data.file, out.data.file, quantile.path, type, ncores)  Adrien Michel committed Nov 11, 2021 8 9 } \arguments{  Adrien Michel committed Nov 11, 2021 10 11 \item{in.data.file}{Path of the file to write the corrected dataset. Dataset will be a RDS file in a vectdata format (see \code{\link{vectorize.list()}})}  Adrien Michel committed Nov 11, 2021 12   Adrien Michel committed Nov 11, 2021 13 14 \item{type}{R or cpp to choose between the R or the C++ implementation (see \code{\link{apply.qm.internal()}} and \code{\link{applyQMCpp()}}).  Adrien Michel committed Nov 11, 2021 15 16 17 18 C++ implementation is faster and recommended, given the package has been installed with OpenMP support.} \item{ncores}{number of cores to be used}  Adrien Michel committed Nov 11, 2021 19 20 21  \item{source.path}{Directory containing the directories with quantile distributions of the training and target datasets (see \code{\link{vectorize.list()}})}  Adrien Michel committed Nov 11, 2021 22 23 24 25 26 27 28 29 } \description{ \code{apply.model} applies a trained QM model (see \code{\link{compute.quantiles()}}) to the dataset given as input. } \author{ Adrien Michel, 2021 (WSL/SLF) }