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% 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{
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apply.model(in.data.file, out.data.file, quantile.path, type, ncores)
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}
\arguments{
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\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()}})}
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\item{type}{R or cpp to choose between the R or the C++ implementation
(see \code{\link{apply.qm.internal()}} and \code{\link{applyQMCpp()}}).
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C++ implementation is faster and recommended, given the package has been
installed with OpenMP support.}

\item{ncores}{number of cores to be used}
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\item{source.path}{Directory containing the directories with quantile distributions of the
training and target datasets (see \code{\link{vectorize.list()}})}
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}
\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)
}